The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography

Cloud-Native Geospatial for Small Teams: How the Utah Geological Survey Did It What happens when a small, technical team at a state agency decides to go cloud native? They don't have terabytes of satellite imagery. They have lots of small datasets, lots of scientists and lots of people who just want to get at the data. In this episode, I talk with Marshall Robinson and Clinton Lunn from the Data Management Program at the Utah Geological Survey. They moved from a traditional ArcGIS Server setup to Postgres and GeoServer, and then to a cloud-native pipeline built on GeoParquet, PMTiles, and a STAC catalog. What I really wanted from this conversation was recipes. Picture a GIS person at a local council (say, here in New Zealand) who's curious about this stuff and needs a proof of concept to take to their manager. Where do they start? We talk about: Why "it's cheaper" isn't a good enough reason to switch, and why it had to work and be better (they still turned off two servers and saved around $25,000 a year) Adding things in slowly instead of ripping everything out. Postgres is still their source of truth, and the cloud-native layer is bolted on top The "why should I care?" question from data users, and how one layer can be exposed 10 different ways so nobody has to change their workflow Using industrial-strength tools on small data, and whether that's worth it How new data gets from the field (or the core warehouse) into their public web apps New staff who know Python, pointing your favourite LLM at a STAC catalog, and why machines finding your data matters Where this is going: Zarr, Icechunk, time-series data and derivative products The stack: Postgres, DuckDB, dbt, GDAL, GitHub Actions / Cloud Build, GeoParquet in bucket storage, STAC, PMTiles and DuckLake Their advice if you want to try this: Start small. Tinker locally. Be kind to yourself. Let an agent help you with best practices and documentation. Before building anything yourself, connect to something that already exists (like STAC Index) to see how it works.   This episode is sponsored by The Cloud Native Geospatial Forum 2026 You can register at 2026.cloudnativegeo.org

We already have a map. Actually, we have lots of maps. So why did Overture build another one? My guest in this episode is Amy Rose, CTO of the Overture Maps Foundation, and she makes a pretty compelling case. Overture isn't trying to replace OpenStreetMap — it sits downstream from it, and from hundreds of other sources, doing the unglamorous work of standardising all of that into something you can actually put into production. A light touch, as Amy puts it. Enough structure to be usable, not so much that the dataset starts trying to be everything for everybody. We spend a good chunk of the conversation on GERS — the Global Entity Reference System. It's a persistent ID stamped on buildings, roads, places, and addresses, which means the same building in OpenStreetMap, Google, Microsoft, and the City of Vancouver all share one identifier. If you've ever wrestled a messy spatial join into submission at 11pm, you'll understand immediately why this matters. And because Overture keeps all the source records, you're not stuck with their choice of polygon. You can go and pick your own. We also get into metadata, which Amy points out has been a dirty word for most of our careers — everybody wants it, nobody wants to write it. Except now it matters more than ever, because the biggest users of geospatial data aren't people anymore. They're machines. Humans are good at filling in missing context. Machines just produce a plausible answer and move on. That's a problem when there's critical infrastructure or human lives involved. Plus: whether "authoritative" still means anything, how signals and evidence might replace traditional contribution models, why there's no best representation of anything — only fit for purpose — and what Amy would add to Overture next if every other problem were magically solved. A really enjoyable conversation, and one that changed how I think about what a map actually is.

Earlier this year I ran a small experiment called the Geospatial Launchpad — six weeks of working closely with a couple of people to help them push their geospatial projects forward. West was one of them. His project is Sentinel Bird (sentinelbird.com): an archive of every Sentinel-2 visit over the Gaza Strip since 2015, with 10-meter resolution imagery for each district, interactive comparison sliders, change-over-time timelapses, and a downloadable press pack — all free, no accounts, no paywall, licensed for anyone to use for anything. In this conversation, we get into what Sentinel Bird is, why West built it, and everything he ran into along the way — the marketing, the SEO, the feedback, all the stuff that has nothing to do with the tech but everything to do with whether a project actually goes anywhere. We talk about: How frustration with English-language media coverage after October 7th turned into a geospatial side project The foundation models West is training on Sentinel-1 SAR and Sentinel-2 optical data for damage detection Making the pipeline location-agnostic, and why tiling across orbital passes is harder than it looks "The agenda is in the data" — building something opinionated without saying a word Why URL structure is the thing you should think hardest about before you hit publish the first time Watching a real human use your site, and how humbling that is Using AI to audit your own site — what was useful, and what advice to ignore The ethics of monetizing a project you'll never put behind a paywall What worked and what didn't in the Geospatial Launchpad, and what I'd change next time West is currently open to work opportunities. If you check out Sentinel Bird and think there's something there, email him at hello @ sentinelbird.com https://sentinelbird.com/  If you're working on your own project and a bit of structure and accountability sounds appealing, there's a link in the show notes — register your interest, and if enough people are keen, I'll run the Launchpad again. Sign up for the next Geospatial Launch Pad     This episode is sponsored by xweather.com Start building with an MCP-ready weather API today. The free developer tier gives you 15,000 free API calls every month. No credit card required. No expiry.Get your free weather API key at xweather.com.

Where does one field end and the next one begin? It sounds trivial right up until you try to answer it. In this episode, I'm joined by Hannah Kerner — Assistant Professor at Arizona State University, AI Lead for NASA Harvest and NASA Acres, and Research Advisor for Taylor Geospatial — to talk about Fields of the World.   This episode is sponsored by the Cloud Native Geospatial Forum. The CNG Forum 2026 runs October 6–9 at Snowbird, Utah — three days of real-world cloud-native geospatial (STAC, COGs, GeoParquet, Zarr, and more) with the teams actually building this stuff at scale, plus a hands-on workshop day to kick things off. Register at https://2026.cloudnativegeo.org

In this episode I'm joined by Apurva Shah, co-founder and CEO of Duality AI, a company building virtual worlds — or "world models" for robots and physical AI systems. Apurva's path here is an unusual one. He spent most of his career in animation, first at Pacific Data Images (which later became DreamWorks) and then over a decade at Pixar. His co-founder, Mike Taylor, comes from the other end of the spectrum entirely: a controls engineer who led field robotics at Caterpillar, deploying house-sized haul trucks at Australian mines. As Apurva puts it, if he's the pixels, Mike is the atoms. We talk about why real-world data, as valuable as it is, is never enough on its own — and how synthetic data can be used to deliberately fill the gaps and biases that creep into any collected dataset. Some of the things we get into: The difference between digital twins and 3D assets and why Duality treats twins as modular building blocks you compose into scenarios, rather than as one monolithic environment How they build environments from the ground up using DEM data, satellite imagery, photogrammetry and biome catalogues and why building them this way means everything is annotated from the start Calibrating virtual sensors against real ones, including synthetic aperture radar, and why sensor noise characteristics matter as much as physics Predicting how a material will behave across the spectrum (infrared, SAR) just from its visual response — and when that prediction breaks down Why "clutter" only becomes clutter once you know what you're looking for, and why it doesn't need to be perfect Modelling star fields for localisation in space, where there are no roads or buildings to navigate by Explicit versus generative world models, and why you need both A project with AWS simulating emergency ambulance routing through a city, complete with autonomous vehicles, traffic control and teleoperated human agents Where Duality is not the right tool molecular scale, virtual patients, drug discovery And yes, a story about robotics companies renting Airbnbs, trashing them, and leaving Towards the end we get into the bigger questions: whether AI takes our jobs or makes us better at them, where the line sits between "good enough" and slop, and why Apurva — a self-described humanist — thinks virtual environments are the one place where human and machine intelligence can genuinely learn from each other. Find out more at duality.ai, or dig into their technical writing at duality.ai/blogs

My guest today is Tyler Reid, co-founder and CTO of Xona, a company building the first commercial satellite navigation system. We get into why Tyler and his team are moving satellites into low Earth orbit and what that unlocks. Stronger signals that can penetrate indoors, more resilient timing infrastructure, and better security against jamming and spoofing. GPS sits 20,000 kilometres out. Xona sits at 1,100, with signals around 100 times stronger and a planned constellation of 258 satellites. Tyler came at this from the autonomous vehicle world at Ford, where the problem was simple enough: ten meters gets you to the store, but it doesn't keep a car in its lane. We also talk about time, and how much of the world quietly depends on GPS to keep its clocks honest. Why countries are suddenly so interested in owning their own infrastructure. And the question Xona gets asked constantly: if you're broadcasting that close to GPS, aren't you the jamming problem? If you're interested in what the future of GNSS might look like, you're really going to enjoy this one. More at xonaspace.com, or reach out to Tyler on LinkedIn.

Vexcel isn't a household name — but you've almost certainly used their data. This aerial imaging company flies low-elevation aircraft across roughly 45 countries, capturing imagery at 7.5cm resolution from five different angles (straight down plus four oblique views), building one of the richest geospatial datasets on Earth. In this episode, Daniel talks with Steve Lombardi, VP of Product at Vexcel, about what happens after the pixels are captured. They dig into object detection and "elements" (pre-extracted features like roof condition, solar panels, and pools), then go deep on Vexcel's newest capability: vector embeddings — essentially a searchable fingerprint for every 100-meter chunk of the planet. Steve explains how customers can search the visible world with a text phrase, an uploaded image, or by simply drawing a box on the map — and get back matching locations anywhere on Earth. They cover how oblique imagery adds context to searches (like finding buildings that "look like a palace"), how customers refine results with a simple thumbs up/down feedback loop, and a fascinating new use case: exposing embeddings as a QGIS tile layer so you can build a personalized, concept-driven heat map — like a custom risk map — without ever touching a database. Topics covered: What makes Vexcel's aerial imagery different from satellite imagery How photogrammetric data enables precise 3D measurement and object detection Object detection vs. vector embeddings — when to use which Custom elements: letting customers define their own objects to detect Searching aerial imagery by text, image, or drawn area Refining search results with a lightweight classifier ("thumb up / thumb down") Change detection over time (the Austin, Texas example) Bringing embeddings into QGIS as a personalized, concept-based tile layer Where aerial imagery and geospatial AI are headed next

What happens when you take cloud native geospatial out of the satellite-and-petabyte world and drop it into a small city with no budget and the world's slowest internet connection? In this episode, Daniel talks with Nissim Lebovits, a city planner turned geospatial data scientist, about his nine months in Argentina building climate risk tools for under-resourced municipalities. Nissim breaks down what "cloud native" actually means in practice (hint: it's really about ease of access), why range requests let a small city grab 100MB instead of downloading a 20GB file, and how a single spatial join — run in about three seconds — revealed that 3 million people are missing from Argentina's own census data. We also get into open building footprints, why QGIS (not Python) is where the real adoption is happening, the adoption gap holding cloud native geo back from small cities in the Global South, vibe-coding a QGIS plugin to finally make Argentina's census data usable, and where cloud native geo is headed over the next five years. This episode is brought to you by the Cloud Native Geospatial Forum. CNG Forum 2026 runs October 6-9 at Snowbird, Utah — three days of real-world cloud native geospatial (STAC, COGs, GeoParquet, Zarr, and more) plus a hands-on workshop day on the 6th. Register at 2026.cloudnativegeo.org.

Jeffrey Martin is the co-founder and CEO of Mosaic, a company building 360-degree camera systems designed specifically for mapping. He's been obsessed with 360 imagery for over 20 years — he built one of the first websites combining panoramic images with a map back in 2005, before Google Street View existed, and he holds a Guinness World Record for a 320-gigapixel image of London stitched together from 52,000 photos. In this episode, we get into what a modern mapping-grade 360 camera actually looks like, and why the difference between rolling shutter and global shutter sensors matters if you care about things like colorizing point clouds or photogrammetry. We also cover the surprisingly long tail of people who need up-to-date street-level imagery — everyone from departments of transport and utilities companies to playground designers and outdoor furniture salespeople. A few things that stood out: Ground-level 360 capture fills gaps that drones and satellites simply can't — occlusion, permissions, and viewing angle all favor eye-level imagery for certain infrastructure work. Companies are taking very different bets on data collection strategy — Mosaic focuses on high-quality, purpose-built capture, while others like Hive Mapper are betting on scale and crowdsourced coverage instead. The camera hardware race may be plateauing, but the real frontier now is what we do with the imagery once it's collected — especially as large language models start being layered on top of geospatial data. Where does this all go next? If AI can eventually parse and reason over an entire country's worth of street-level imagery, what does that unlock — and who ends up owning that layer of the map?

Open source software runs a huge chunk of the geospatial world — but somebody still has to pay for it. In this episode I sit down with Marco Bernasocchi creator of QField and CEO of OpenGIS.ch, to dig into the awkward question most open source projects avoid: how do you keep something free and open while paying real people to build and maintain it? Marco has been in the open source world since 2007, and he's grown QField into a tool with over two million downloads and a team of 14 behind it. We talk through how the money actually works — from sponsored feature development, to donations, to the cloud service that now funds most of what they do. Marco makes a compelling case that the real product isn't the software at all; it's convenience. You can always run it yourself. Paying just makes life easier — and keeps the project alive for everyone who can't. We also get into why he refuses to say "free software," what maintainer burnout really looks like, and his advice for any developer quietly drowning in a project they love but can't afford to keep running. A candid conversation about money, sustainability, and being a good citizen in the open source ecosystem.

Ian Schuler is the CEO of Development Seed — the team behind a lot of the open source tooling that quietly holds up the geospatial world. He's been at the helm for over a decade, and in this conversation, we dig into what he calls the great retooling: the idea that cloud-native geospatial is about to flip from an emerging pattern to the dominant one, and that AI is the thing tipping it over the edge. The argument is simple — agents want to discover your data, query it, transform it, and hand back an answer. If your data isn't in a format they can reach, you're simply not part of the conversation anymore. A really enjoyable one. I hope you get as much out of it as I did. Register for the forum 👉 https://2026.cloudnativegeo.org — This episode is sponsored by the Cloud Native Geospatial Forum. The CNG Forum 2026 runs October 6–9 at Snowbird, Utah — three days of real-world cloud-native geospatial (STAC, COGs, GeoParquet, Zarr, and more) with the teams actually building this stuff at scale, plus a hands-on workshop day to kick things off. Register at https://2026.cloudnativegeo.org

What is Earth observation, really — and why, after fifty years of satellite imagery, is it still not "mainstream"? In this episode, I'm joined by Aravind Ravichandran, founder of TerraWatch, an independent research and advisory firm focused entirely on Earth observation. Aravind writes the TerraWatch newsletter, runs the EO Summit, and spends his time thinking about the strategy and economics of the industry more deeply than just about anyone. We start with a deceptively simple question — is Earth observation even an industry? — and end up somewhere more interesting: Aravind's argument that when the technology truly succeeds, it becomes invisible, quietly embedded in agriculture, insurance, energy, and defense the same way weather satellites already are. Along the way, we get into: Why 60+ countries are now building their own satellite constellations, and whether they'll still exist in five years What Planet restricting imagery access really means — and why Aravind thinks they were "punished for doing something progressive" The technology is actually moving the needle: hyperspectral data going free, AI foundation models, edge computing on satellites, and inter-satellite laser links Which use cases are genuinely picking up (utilities, parametric insurance) — and which were always hype (counting cars in parking lots) The defense paradox: how the industry that built Earth observation may also be the biggest thing holding back its commercial future Some open questions we sit with: If satellite data is critical infrastructure, what happens when someone turns it off? Should high-resolution imagery of the whole world be open — and what are the privacy and security costs if it is? And can sixty countries ever pool their data, or will sovereignty always trump logic?

Ryan Shields has one of the most interesting careers in geospatial — from remote sensing for conservation in the Caribbean, to disaster response data engineering with FEMA, to his current role turning spatial data into animation assets for Johnny Harris's YouTube channel at New Press. In this episode, Ryan counts down the 10 tools he's using right now to tell map stories that reach millions of viewers. We cover Felt, PostGIS on Crunchy Bridge, Geo Layers 3 for After Effects, CShapes for historical borders, Natural Earth, MapTiler, Mapshaper, the new GDAL pipeline syntax, GRASS GIS, and how he's stitching it all together with Claude Code and VS Code. Along the way we get into how LLMs are changing geospatial workflows, why command-line tools are well-suited to AI agents, the limits of de facto vs de jure borders in historical datasets, and how better tooling is making data journalism viable for small communities that newsrooms usually overlook. Whether you're a cartographer, data engineer, journalist, or just map-curious, this one is packed with links worth chasing.   Tools & resources mentioned in this episode Felt — https://felt.com PostGIS — https://postgis.net Crunchy Bridge — https://www.crunchybridge.com Geo Layers 3 (After Effects extension) — https://aescripts.com/geolayers/ ⚠️ verify CShapes (historical borders dataset) — https://icr.ethz.ch/data/cshapes/ ⚠️ verify Open Historical Map — https://www.openhistoricalmap.org Natural Earth — https://www.naturalearthdata.com Eduard (Swiss-style hillshading app) — https://www.eduard.earth ⚠️ verify Shaded Relief (Tom Patterson) — https://www.shadedrelief.com MapTiler — https://www.maptiler.com MapTiler Engine — https://www.maptiler.com/engine/ EPSG.io — https://epsg.io Mapshaper — https://mapshaper.org GDAL — https://gdal.org GRASS GIS — https://grass.osgeo.org QGIS — https://qgis.org DBeaver — https://dbeaver.io Claude Code — https://claude.com/claude-code ⚠️ verify VS Code — https://code.visualstudio.com Geodata Viewer (VS Code extension) — search "Geodata Viewer" in the VS Code marketplace PAI – Personal AI Infrastructure (Daniel Miessler) — https://github.com/danielmiessler ⚠️ verify exact repo Deep State Map (Ukraine conflict) — https://deepstatemap.live Johnny Harris (YouTube) — https://www.youtube.com/@johnnyharris Projects I'm working on Quick Map Tools — https://quickmaptools.com Hunting NZ — https://huntingnz.com NZ Elevation Tools — https://nzelevationtools.com Smart Query Tools — https://smartquerytools.com

Nadine Alameh is back — former CEO of the Open Geospatial Consortium, and now CEO and co-founder of Lunate AI, a six-month-old company sitting right at the messy intersection of geospatial and AI. In this conversation, Nadine breaks down the three types of clients she's seeing right now: government agencies standing at the edge of the river, wondering whether to jump in, startups from outside the geospatial world stumbling in with big ideas, and organizations that know they need to modernize but don't know who to call. We get into why the real value today is in experience and advisory rather than raw coding, why "moving up the stack" matters more than ever, and how AI agents are quietly reshaping everything — from how satellites get tasked to how dashboards (or whatever replaces them) get built. We also talk about the death of the one-size-fits-all dashboard, world models and simulations, why trust and guardrails are the actual hard work, and what it takes to go from a flashy proof-of-concept to something a bank can rely on every morning. If you're a GIS professional thinking about where to position yourself, a startup founder wandering into the geospatial world, or someone trying to figure out how AI fits into your workflows — this one's for you.

What happens when you put professional-grade aerial mapping in the hands of the people who actually live in the places being mapped? In this episode, I'm joined by Rebecca Firth, Executive Director of the Humanitarian OpenStreetMap Team (HOT) — a global community of around 750,000 people building free and open-source maps in the places that need them most. We dig into HOT's Drone Tasking Manager: a tool that lets local residents, using low-cost consumer drones, capture professional-quality aerial imagery of their own communities. Rebecca explains how it works under the hood, how dozens of pilots can coordinate to produce a single seamless mosaic, and the assumptions her team got wrong along the way — from over-engineered task locking to worrying about the wrong problems entirely. We also talk about what this looks like on the ground in Freetown, Sierra Leone, where the same drone imagery is now being used across seven city departments — for waste collection planning, disability access, flood mitigation, and soon, thermal mapping during heat waves to support local-led climate adaptation. If you care about mapping, drones, open data, or the simple idea that local people with local tools can solve problems faster than anyone flying in from outside — this one's for you.   Thank you to today's sponsor, Geo Business - Registration is free.

This episode examines the Common Space initiative, a non-profit project dedicated to building and launching high-resolution optical satellites designed specifically for humanitarian purposes, such as aiding populations at risk from climate events and conflict.  Although there are over a thousand Earth observation satellites currently in orbit, high-resolution imagery remains largely inaccessible to humanitarians, journalists, and civil rights groups due to high costs, restrictive licensing, and the prioritization of defense and intelligence tasking.  Common Space aims to bridge the gap between low-resolution public goods (like Landsat and Sentinel) and expensive commercial options by offering 50 to 70-centimeter resolution imagery with open licensing.  The project plans to utilize a "club good" funding model, where humanitarian groups can access the data for free, while commercial and government entities pay to participate to fund the system's continued operations. How will a community-driven governance model successfully navigate the ethical risks and potential misuse of releasing high-resolution conflict data in real-time?   Learn more about Commonspace here https://www.commonspace.world/    Or connect with the founders here https://www.linkedin.com/in/billfgreer/  https://www.linkedin.com/in/rhiannan-price/

I've been playing around with a lot of large language models lately, and it is absolutely fascinating to watch them work. But what happens when you bring that directly into QGIS? Right now, AI in the geospatial industry is a lot like a fast, enthusiastic new intern, incredibly helpful, and sometimes completely wrong, but improving at a rate that no human can compete with.  As we hand more of our geoprocessing tasks over to these algorithms, and computing becomes more pervasive, are our own GIS skills becoming obsolete? Or are we just unlocking radically different opportunities to rethink our careers?

Geospatial Product Swiss Army Knife 1. The "Build It and They Won't Come" Trap We have all seen it: a talented geospatial professional spends months—perhaps years—perfecting a technically sophisticated web map or a niche data service, only to release it to a deafening silence. In our industry, the "build it and they will come" philosophy is a fast track to zero traction. Precision is the enemy of progress when it is applied to the wrong problem. Daniel and Stella Blake Kelly explored a remedy for this pattern. Stella—a New Zealand-born, Sydney-based strategist and founder of the consultancy Cartisan—didn’t start with a master plan. She "fell into" the industry after being inspired by a lecturer with bright blue hair and a passion for GIS that rivaled a Lego builder’s creativity. Today, she helps organizations move from "making things" to "building products that matter" using a framework she calls the Product Swiss Army Knife. -------------------------------------------------------------------------------- 2. The 7-Step Framework: More Than Just a Map Many geospatial experts suffer from a technology-first bias, prioritizing data accuracy over strategic utility. To counter this, Stella advocates for a disciplined, seven-tool toolkit designed to bridge the gap between GIS and Product Design: Vision: Establish a clear statement of what you are building and why it needs to exist. User Needs: Move beyond assumptions to identify real users and their specific friction points. Market & Context: Analyze the existing ecosystem (competitors, data, and workflows) to find your gap. Features: Ruthlessly prioritize "must-haves" to define a lean Minimum Viable Product (MVP). Prototypes & User Flows: Map out the user’s journey through the service before writing a line of code. Proof of Concept: Create a tangible, working version to prove the technical and market logic. Launch & Learn: Release early to gather real-world data and iterate based on evidence. This structure forces builders to treat the "spatial" element as a solution rather than the entire product. To illustrate User Needs (Tool #2), Stella suggests using formal User Stories to step out of the technical mindset: "As a solar panel marketer, I want to find potential customers with enough roof surface area so that I can reach out to them and provide an accurate quote." By grounding the project in a specific human problem, the developer stops building for themselves and starts building for the market. As Stella notes: "The thing about the product Swiss Army knife... is that it can be applied to almost any situation where there is an end consumer, where somebody is going to use the thing, the service that you make." -------------------------------------------------------------------------------- 3. The "200 Tools" Strategy: Programmatic Market Validation Daniel shared an unconventional approach to product discovery that serves as a masterclass in Market Context (Tool #3). Leveraging AI, he has built nearly 200 simple geospatial tools—such as a "Roof Area Calculator"—not as final products, but as a "sandbox" for discovery. This is Programmatic Market Validation. Instead of starting with a complex SaaS model, Daniel uses these micro-tools to find "winners" via organic search traffic. By observing where the internet already has unsolved spatial queries, he lets the market dictate which products deserve a full-scale build. In this new landscape, the barrier to entry has shifted: the competitive advantage is no longer "coding ability"—it is strategic experimentation. -------------------------------------------------------------------------------- 4. Not All Traffic is Equal: The High-Value Keyword Insight One of the most surprising takeaways from this experimentation is the direct link between specific geospatial problems and commercial value. A general GIS data tool might get thousands of views, but a "Roof Area Calculator" generates significantly higher programmati

Why Machine-Writing Code is the Best (and Most Dangerous) Thing for Geospatial:   The current discourse surrounding AI coding is nothing if not polarized. On one side, the technofuturists urge us to throw away our keyboards; on the other, skeptics dismiss Large Language Models (LLMs) as little more than "fancy autocomplete" that will never replace a "real" engineer. Both sides miss the nuanced reality of the shift we are living through right now.   I recently sat down with Matt Hansen, Director of Geospatial Ecosystems at Element 84, to discuss this transition. With a 30-year career spanning the death of photographic film to the birth of Cloud-Native Geospatial, Hansen has a unique vantage point on how technology shifts redefine our roles. He isn’t predicting a distant future; he is describing a present where the barrier between an idea and a functioning tool has effectively collapsed.   The "D" Student Who Built the Future Hansen’s journey into the heart of open-source leadership began with what he initially thought was a terminal failure. As a freshman at the Rochester Institute of Technology, he found himself in a C programming class populated almost entirely by seasoned professionals from Kodak. Intimidated and overwhelmed by the "syntax wall," he withdrew from the class the first time and scraped by with a "D" on his second attempt. For years, he believed software simply wasn't his path. Today, however, he is a primary architect of the SpatioTemporal Asset Catalog (STAC) ecosystem and a major open-source contributor. This trajectory is the perfect case study for the democratizing power of AI: it allows the subject matter expert—the person who understands "photographic technology" or "imaging science"—to bypass the mechanical hurdles of brackets and semi-colons. "I took your class twice and thought I was never software... and now here I am like a regular contributor to open source software for geospatial." — Matt Hansen to his former professor.   The Rise of "Vibe Coding" and the Fragmentation Trap   We are entering the era of "vibe coding," where developers prompt AI based on a general description or "vibe" of what they need. While this is exhilarating for the individual, it creates a systemic risk of "bespoke implementations." When a user asks an AI for a solution without a deep architectural understanding, the machine often generates a narrow, unvetted fragment of code rather than utilizing a secure, scalable library. The danger here is a catastrophic loss of signal. If thousands of users release these AI-generated fragments onto platforms like GitHub, we risk drowning out the vetted, high-quality solutions that the community has spent decades building. We are creating a "sea of noise" that could make it harder for both humans and future AI models to identify the standard, proper way to solve a problem.   Why Geospatial is Still "Special" (The Anti-meridian Test)   For a long time, the industry mantra has been "geospatial isn’t special," pushing for spatial data to be treated as just another data type, like in GeoParquet. However, Hansen argues that AI actually proves that domain expertise is more critical than ever. Without specific guidance, AI often fails to account for the unique edge cases of a spherical world. Consider the "anti-meridian" problem: polygons crossing the 180th meridian. When asked to handle spatial data, an AI will often "brute force" a custom logic that works for a small, localized dataset but fails the moment it encounters the wrap-around logic of a global scale. A domain expert knows to direct the AI toward Pete Kadomsky’s "anti-meridian" library. AI is not a subject matter expert; it is a powerful engine that requires an expert navigator to avoid the "Valley of Despair."   Documentation is Now SEO for the Machines   We are seeing a counterintuitive shift in how we value documentation. Traditionally, README files and tutorials were written by humans, for humans. In the age of AI, doc

How can you accurately aggregate and compare point-based data from different parts of the world? When analyzing crime rates, population, or environmental factors, how do you divide the entire globe into equal, comparable units for analysis?   For data scientists and geospatial analysts, these are fundamental challenges. The solution lies in a powerful class of tools called Discrete Global Grid Systems (DGGS). These systems provide a consistent framework for partitioning the Earth's surface into a hierarchy of cells, each with a unique identifier. The most well-known systems, Google's S2 and Uber's H3, have become industry standards for everything from database optimization to logistics.   However, these systems come with inherent trade-offs. Now, a new DGGS called A5 has been developed to solve some of the critical limitations of its predecessors, particularly concerning area distortion and analytical accuracy.   Why Gridding the Globe is Harder Than It Looks The core mathematical challenge of any DGGS is simple to state but difficult to solve: it is impossible to perfectly flatten a sphere onto a 2D grid without introducing some form of distortion. Think of trying to apply a perfect chessboard or honeycomb pattern to the surface of a ball; the shapes will inevitably have to stretch or warp to fit together without gaps.   All DGGS work by starting with a simple 3D shape, a polyhedron, and projecting its flat faces onto the Earth's surface. The choice of this initial shape and the specific projection method used are what determine the system's final characteristics. As a simple analogy, consider which object you’d rather be hit on the head with: a smooth ball or a spiky cube? The ball is a better approximation of a sphere. When you "inflate" a spiky polyhedron to the size of the Earth, the regions nearest the sharp vertices get stretched out the most, creating the greatest distortion.   A Quick Look at the Incumbents: S2 and H3   To understand what makes A5 different, it's essential to have some context on the most popular existing systems.   Google's S2: The Cube-Based Grid The S2 system is based on projecting a cube onto the sphere. On each face of this conceptual cube, a grid like a chessboard is applied. This approach is relatively simple but introduces significant distortion at the cube’s vertices, or "spikes." As the grid is projected onto the sphere, the cells near these vertices become stretched into diamond shapes instead of remaining square. S2 is widely used under the hood for optimizing geospatial queries in database systems like Google BigQuery.   Uber's H3: The Hexagonal Standard Uber's H3 system starts with an icosahedron—a 20-sided shape made of triangles. Because an icosahedron is a less "spiky" shape than a cube, H3 suffers from far less angular distortion. Its hexagonal cells look more consistent across the globe, making it popular for visualization. H3's immense success is also due to its excellent and user-friendly ecosystem of tools and libraries, making it easy for developers to adopt. However, H3 has one critical limitation for data analysis: it is not an equal-area system. This was a deliberate trade-off, not a flaw; H3 was built by a ride-sharing company trying to match drivers to riders, a use case where exact equal area doesn't particularly matter. To wrap a sphere in hexagons, you must also include exactly 12 pentagons—just like on a soccer ball. If you look closely at a football, you'll see the pentagonal panels are slightly smaller than the hexagonal ones. This same principle causes H3 cells to vary in size. The largest and smallest hexagons at a given resolution can differ in area by a factor of two, meaning that comparing raw counts in different cells is like comparing distances in miles and kilometers without conversion. For example, cells near Buenos Aires are smaller because of their proximity to one of the system's core pentagons, creating a potential source of error if not prope

The Open-Source Conundrum   Many successful open-source projects begin with passion, but the path from a community-driven tool to a sustainable business is often a trap.   The most common route—relying on high-value consulting contracts—can paradoxically lead to operational chaos. Instead of a "feast or famine" cycle, many companies find themselves with more than enough work, but this success comes at a cost: a fragmented codebase, an exhausted team, and a growing disconnect from the core open-source community.   This episode deconstructs a proven playbook for escaping this trap: the strategic transition from a service-based consultancy to a product-led company.   Through the story of Jeroen Ticheler and his company, GeoCat, we will analyze how this pivot creates a more stable business, a healthier open-source community, and ultimately, a better product for everyone.

Open-source software is often described as "free," a cornerstone of the modern digital world available for anyone to download, use, and modify. But this perception of "free" masks a growing and invisible cost—not one paid in dollars, but in the finite attention, time, and mounting pressure placed on the volunteer and community maintainers.   This hidden tax is most acute when it comes to security.   Jody from Geocat, a long-time contributor to the popular GeoServer project, pulled back the curtain on the immense strain that security vulnerabilities place on the open-source ecosystem.   His experiences reveal critical lessons for anyone who builds, uses, or relies on open-source software.

What if communities could map their own worlds using low-cost drones and open AI models instead of waiting for expensive satellite imagery? In this episode with Leen from HOT (Humanitarian OpenStreetMap Team), we explore how they're putting open mapping tools directly into communities' hands—from $500 drones that fly in parallel to create high-resolution imagery across massive areas, to predictive models that speed up feature extraction without replacing human judgment. Key topics: Why local knowledge beats perfect accuracy The drone tasking system: how multiple pilots map 80+ square kilometers simultaneously AI-assisted mapping with humans in the loop at every step Localizing AI models so they actually understand what buildings in Chad or Papua New Guinea look like The platform approach: plugging in models for trees, roads, rooftop material, waste detection, whatever communities need The tension between speed and OpenStreetMap's principles Why mapping is ultimately a power game—and who decides what's on the map

This conversation with Jed Sundwall, Executive Director of Radiant Earth, starts with a simple but crucial distinction: the difference between data and data products. And that distinction matters more than you might think. We dig into why so many open data portals feel like someone just threw up a bunch of files and called it a day. Sure, the data's technically "open," but is it actually useful? Jed argues we need to be way more precise with our language and intentional about what we're building. A data product has documentation, clear licensing, consistent formatting, customer support, and most importantly - it'll actually be there tomorrow. From there, we explore Source Cooperative, which Jed describes as "object storage for people who should never log into a cloud console." It's designed to be invisible infrastructure - the kind you take for granted because it just works. We talk about cloud native concepts, why object storage matters, and what it really means to think like a product manager when publishing data. The conversation also touches on sustainability - both the financial kind (how do you keep data products alive for 50 years?) and the cultural kind (why do we need organizations designed for the 21st century, not the 20th?). Jed introduces this idea of "gazelles" - smaller, lighter-weight institutions that can move together and actually get things done. We wrap up talking about why shared understanding matters more than ever, and why making data easier to access and use might be one of the most important things we can do right now.

Reflections from the FOSS4G 2025 conference    Processing, Analysis, and Infrastructure (FOSS4G is Critical Infrastructure) The high volume of talks on extracting meaning from geospatial data—including Python workflows, data pipelines, and automation at scale—reinforced the idea that FOSS4G represents critical infrastructure. AI Dominance: AI took up a lot of space at the conference. I was particularly interested in practical, near-term impact talks like AI assisted coding and how AI large language models can enhance geospatial workflows in QGIS. Typically, AI discussions focus on big data and earth observation, but these topics touch a larger audience. I sometimes wonder if adding "AI" to a title is now like adding a health warning: "Caution, a machine did this". Python Still Rules (But Rust is Chatting): Python remains the pervasive, default geospatial language. However, there was chatter about Rust. One person suggested rewriting QGIS in Rust might make it easier to attract new developers. Data Infrastructure, Formats, and Visualization When geospatial people meet, data infrastructure—the "plumbing" of how data is stored, organized, and accessed—always dominates. Cloud Native Won: Cloud native architecture captured all the attention. When thinking about formats, we are moving away from files on disk toward objects in storage and streaming subsets of data. Key cloud-native formats covered included COGs (Cloud Optimized GeoTIFFs), Zarr, GeoParquet, and PMTiles. A key takeaway was the need to choose a format that best suits the use case, defined by who will read the file and what they will use the data for, rather than focusing solely on writing it. The Spatial Temporal Asset Catalog (STAC) "stole the show" as data infrastructure, and DuckDB was frequently mentioned. Visualization is moving beyond interactive maps and toward "interactive experiences". There were also several presentations on Discrete Global Grid Systems (DGGS). Standards and Community Action Standards Matter: Standards are often "really boring," but they are incredibly important for interoperability and reaping the benefits of network effects. The focus was largely on OGC APIs replacing legacy APIs like WMS and WFS (making it hard not to mention PyGeoAPI). Community Empowerment: Many stories focused on community-led projects solving real-world problems. This represents a shift away from expert-driven projects toward community action supported by experts. Many used OSM (OpenStreetMap) as critical data infrastructure, highlighting the need for locals to fill in large empty chunks of the map. High-Level Takeaways for the Future If I had to offer quick guidance based on the conference, it would be: Learn Python. AI coding is constantly improving and worth thinking about. Start thinking about maps as experiences. Embrace the Cloud and understand cloud-native formats. Standards matter. AI is production-ready and will be an increasingly useful interface to analysis. Reflections: What Was Missing? The conference was brilliant, but a few areas felt underrepresented: Sustainable Funding Models: I missed a focus on how organizations can rethink their business models to maintain FOSS4G as critical infrastructure without maintainers feeling their time is an arbitrage opportunity. Niche Products: I would have liked more stories about side hustles and niche SAS products people were building, although I was glad to see the "Build the Thing" product workshop on the schedule. Natural Language Interface: Given the impact natural language is having on how we interact with maps and geo-data, I was surprised there wasn't more dedicated discussion around it. I believe it will be a dominant way we interact with the digital world. Art and Creativity: Beyond cartography and design talks, I was surprised how few talks focused on creative passion projects built purely for the joy of creation, not necessarily tied to making a part of something bigger.

Karl returns to the Mapscaping podcast to discuss his latest venture, Tyche Insights - a platform aimed at building a global community of geospatial storytellers working with open data. In this conversation, we explore the evolution from his previous company, Building Footprint USA (acquired by Lightbox), to this new mission of democratizing public data storytelling. Karl walks us through the challenges and opportunities of open data, the importance of unbiased storytelling, and how geospatial professionals can apply their skills to analyze and share insights about their own communities. Karl shares his vision for creating something akin to Wikipedia, but for civic data stories - complete with style guides, editorial processes, and community collaboration.   Featured Links Tyche Insights: Main website: https://tycheinsights.com Wiki platform: https://wiki.tycheinsights.com Example project: https://albanydatastories.com Mentioned in Episode: USAFacts: https://usafacts.org QField Partner Program: https://qfield.org/partner Open Data Watch: (monitoring global open data policies)

AI Slop: An Experiment in Discovery Solo Episode Reflection: I'm back behind the mic after about a year-long break. Producing this podcast takes more time than you might imagine, and I was pretty burnt out. The last year brought some major life events, including moving my family back to New Zealand from Denmark, dealing with depression, burying my father, starting a new business with my wife, and having a teenage daughter in the house. These events took up a lot of space. The Catalyst for Return: Eventually, you figure out how to deal with grief, stop mourning the way things were, and focus on the way things could be. When this space opened up in my life, AI came into the picture. AI got me excited about ideas again because for the first time, I could just build things myself without needing to pitch ideas or spend limited financial resources. On "AI Slop": I understand why some content is called "slop," but for those of us who see AI as a tool, I don't think the term is helpful. We don't refer to our first clumsy experiments with other technologies—like our first map or first lines of code—as slop. I believe that if we want to encourage curiosity and experimentation, calling the results of people trying to discover what's possible "slop" isn't going to help.   My AI Experimentation Journey My goal in sharing these experiments is to encourage you to go out and try AI yourself. Phase 1: SEO and Content Generation My experimentation began with generating SEO-style articles as a marketing tool. As a dyslexic person, I previously paid freelancers thousands of dollars over the years to help create content for my website because it was too difficult or time-consuming for me to create myself. Early Challenges & Learning: My initial SEO content wasn't great, and Google recognized this, which is why those early experiments don't rank in organic search. However, this phase taught me about context windows, the importance of prompting (prompt engineering), and which models and tools to use for specific tasks. Automation and Agents: I played around with automation platforms like Zapier, make.com, and n8n. I built custom agents, starting with Claude projects and custom GPTs. I even experimented with voice agents using platforms like Vappy and 11 Labs. Unexpected GIS Capabilities: During this process, I realized you can ask platforms like ChatGPT to perform GIS-related data conversions (e.g., geojson to KML or shapefile using geopandas), repro data, create buffers around geometries, and even upload a screenshot of a table from a PDF and convert it to a CSV file. While I wouldn't blindly trust an LLM for critical work, it's been interesting to learn where they make mistakes and what I can trust them for. AI as a Sparring Partner: I now use AI regularly to create QGIS plugins and automations. Since I often work remotely as the only GIS person on certain projects, I use AI—specifically talking to ChatGPT via voice on my phone—as a sparring partner to bounce ideas off of and help me solve problems when I get stuck. Multimodal Capabilities: The multimodal nature of Gemini is particularly interesting; if you share your screen while working in QGIS, Gemini can talk you through solving a problem (though you should consider privacy concerns).   The Shift to Single-Serve Map Applications I noticed that the digital landscape was changing rapidly. LLMs were becoming "answer engines," replacing traditional search on Google, which introduced AI Overviews. Since these models no longer distribute traffic to websites like mine the way they used to, I needed a new strategy. The Problem with Informational Content: Informational content on the internet is going to be completely dominated by AI. The Opportunity: Real Data: AI is great at generating content, but if you need actual data—like contours for your specific plot of land in New Zealand—you need real data, not generated data. New Strategy: My new marketing strategy is to create targeted

Jonathan Wagner, CEO of Scribble Maps, is back on the podcast, and this time we're talking about Scribble—an AI agent he's built into his platform. Not a chatbot, an agent. There's a difference, and we get into that. https://mapscaping.com/podcast/the-business-of-web-maps/  So far, Scribble has access to 140 tools. It can view your map, select tools, build plugins, fetch data, and handle onboarding and customer education.   But here's the thing—should you care?   I think you should, because we're going to see more and more of these things. And whether you like it or not, for a lot of people, this is going to be the way they interact with geospatial data. I don't think we can put the genie back in the bottle. I personally, I'm not entirely sure I would if I could.   Yeah, sure, there's a lot of uncertainty around what these things can do and how they're going to impact us. I get that. I feel it too. But we can't afford to stick our heads in the sand and pretend like it's not happening.   In this conversation, Jonathan walks through why he built Scribble (spoiler: his wife was expecting and he needed to solve an onboarding problem), the real risks of adding AI to your product, and the technical decisions behind using Gemini over OpenAI. We also talk about privacy concerns, the Model Context Protocol (MCP), and what this all means for the future of GIS.   We touch on the QGIS MCP server, the democratization of mapping tools, and when maps aren't actually the answer. It's an honest look at where we are with AI agents in geospatial, from someone who's actually building one.   https://en.wikipedia.org/wiki/Lojban    https://github.com/jjsantos01/qgis_mcp    How's that?

Exploring the Evolution and Impact of Mapillary with Ed from Meta.  Topics include Ed's journey with Mapillary, the process of uploading and utilizing street-level imagery, and the integration with OpenStreetMap. Ed talks about the challenges of mapping with various devices, the role of community contributions, and future potentials in mapping technology, such as using neural radiance fields (NeRFs) for creating immersive 3D scenes. The episode provides insights into how Mapillary is advancing geospatial data collection and usage. 00:00 Introduction to the Map Scaping Podcast 00:57 Meet Ed: Product Manager at Meta 02:09 Ed's Journey with Mapillary 03:59 What is Mapillary? 07:00 The Evolution of 360 Cameras 09:20 Uploading Imagery to Mapillary 14:10 Building a 3D Model of the World 19:10 Meta's Use of Map Data 21:24 The Importance of Community in Mapping 24:15 The Importance of Authoritative Data 24:49 Meta's Contributions to Open Source Geo World 25:27 Real-World Applications: Vietnam's B Group 28:16 Innovative Mapping in Detroit 31:38 Future of Mapping: Lidar and Beyond 32:20 Exploring Neural Radiance Fields (NeRFs) 35:40 Challenges and Innovations in Mapping Technology 45:25 Community Contributions and Future Directions 46:37 Closing Remarks and Contact Information   Previous episodes that you might find interesting https://mapscaping.com/podcast/scaling-map-data-generation-using-computer-vision/ https://mapscaping.com/podcast/the-rapid-editor/ https://mapscaping.com/podcast/overture-maps-and-the-daylight-distribution/

Telematics Data is Reshaping Our Understanding of Road Networks In this episode MIT Professor Hari Balakrishnan explains how Cambridge Mobile Telematics (CMT) is transforming traditional road network analysis by layering dynamic behavioural data onto static map geometries.  Telematics data creates "living maps" that go beyond traditional road geometry and attributes. By collecting movement data from 45 million users through phones and IoT devices, CMT has developed sophisticated models that can: - Generate dynamic risk maps showing crash probability for every road segment globally- Detect infrastructure issues that aren't visible in traditional mapping (like poorly placed bus stops)- Validate and correct map attributes like speed limits and lane connectivity- Differentiate between overpasses and intersections using movement patterns- Create contextual understanding of road segments based on actual usage patterns Particularly interesting for GIS professionals is CMT's approach to data fusion, combining traditional map geometry with temporal movement data to create predictive models. This has practical applications from infrastructure planning to autonomous vehicle navigation, where understanding the cultural context of road usage proves as important as precise geometry. The episode challenges traditional static approaches to road network mapping, suggesting that the future lies in dynamic, behavior-informed spatial data models that can adapt to changing conditions and usage patterns. For anyone working with transportation networks or smart city initiatives, this episode provides valuable insights into how movement data is changing our understanding of road infrastructure and spatial behaviour.   Connect with Hari on LinkedIn! https://www.linkedin.com/in/hari-balakrishnan-0702263/ Cambridge Mobile Telematics https://www.cmtelematics.com/   BTW,  I keep busy creating free mapping tools and publishing them there https://mapscaping.com/map-tools/ swing by and take a look!

In this week’s episode, I’m thrilled to welcome back Ariel Seidman, founder of HiveMapper. Ariel was my very first podcast guest back in 2019, and HiveMapper has come a long way since then! We explore how HiveMapper has evolved from a drone-based mapping system to a cutting-edge platform collecting street-level data at a global scale. Ariel shares the challenges of scaling large-scale mapping efforts, the pivot to building their own hardware, and the role of blockchain-based incentives in driving adoption. Here are just a few topics we cover: Why HiveMapper shifted focus from drones to street-level mapping. The power of combining hardware and software to solve mapping challenges. How HiveMapper has already mapped 28% of the global road network. The revolutionary edge computing and data filtering techniques driving efficiency. What it takes to compete with industry giants like Google Maps. Whether you're fascinated by the intersection of geospatial technology and innovation or looking for insights into scaling impactful startups, this episode is packed with value. Let me know your thoughts or hit reply if you’d like to discuss the episode!   https://beemaps.com/ Connect with Ariel here https://www.linkedin.com/in/aseidman/   PS I have just finished creating a web-based tool that lets you explore and download OpenStreetMap data, It is a bit different from other tools and I would appreciate some feedback.  https://mapscaping.com/openstreetmap-category-viewer/

Tracking elephants in Southern Africa’s Kavango-Zambezi (KAZA) region, the largest transfrontier conservation area in the world. Lead scientist Robin Naidoo from the World Wildlife Fund-US explains the complex, cross-border collaboration required to understand elephant movements across vast landscapes and the role of GNSS.   Connected with Robin  https://www.worldwildlife.org/experts/robin-naidoo   Read more information about this study here https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.14746   https://news.mongabay.com/2024/09/jumbo-collaring-effort-reveals-key-elephant-movement-corridors/   Check out https://www.movebank.org/

Today's episode touches on some pretty big topics like Imposter Syndrome, Mentorship, Career Progression, Adaptability and Diversity Today you are going to hear two stories from two very different voices. Two brilliant people who happen to be women in geospatial.    Ta Taneka https://www.linkedin.com/in/ta-taneka/ Mary Murphy https://www.linkedin.com/in/mary-murphy-12319433/   You can check out the GIS Directions Podcast here: https://esriaustralia.com.au/gis-directions-podcast or search for GIS Directions where every you listen to podcasts   Recommended Podcast Episodes Getting where you want to go in your geospatial career Mentorship leadership and career advice Mentorship leadership and career advice

In this episode, Marco Bernasconi, co-founder and CEO of OPENGIS.ch, introduces us to QField, an open-source mobile application designed for field data collection in conjunction with QGIS. Marco shares his journey in developing QField and discusses its seamless integration with QGIS, allowing users to capture, survey, and manage geospatial data on various mobile devices. We also discuss the technical aspects of QField, including its user-friendly interface, the ability to connect with external sensors, and the recent introduction of QField Cloud for enhanced data synchronization and management. Marco highlights the application’s diverse use cases, from citizen science initiatives to archaeological documentation and utility inspections, demonstrating its potential to transform data collection processes across various industries. More information on Qfield:  https://qfield.org/ https://qfield.cloud/ Or https://www.opengis.ch/#contact      On a personal note, I have been working as a freelance Geospatial consultant for some time now and one of my projects is slowly winding down, which is why I am looking for new projects to get involved in! If you need expertise in Geospatial consultancy, GIS management or the marketing of geospatial products and services Please reach out! https://www.linkedin.com/in/danielodonohue/ or contact me here info@mapscpaing.com

This is the story of Priscilla Cole, and what she did when she discovered that her ambitions were bigger than the tools she was using! Connect with Priscilla here! https://www.linkedin.com/in/priscilla-cole-5892549/   Recommended Listening The Way You Talk About Your Skills Is Costing You Money   Geospatial Consulting As A Business And Career   Mid-Life Career Change   Getting Where You Want To Go In Your Career   Applying For A Job, Getting Picked and Negotiating   Mentorship Leadership And Career Advice

In this episode, I'm joined by Konstantine Klemmer, a researcher at Microsoft, to dive deep into the fascinating world of GeoAI. Konstantine introduces us to Satclip, a cutting-edge model that encodes geographic locations based on satellite images. We discuss how Satclip works, the data it uses, and its potential applications, particularly in low-resource settings and predictive modeling. Whether you're into AI, geography, or just curious about the intersection of these fields, this episode is packed with insights. Key Takeaways: What is Satclip?: Learn about Satclip's location encoding, a neural network that converts geographic coordinates into numerical representations based on satellite images. Data and Training: Understand how Satclip is trained using Sentinel-2 satellite images and how it captures unique geographic features. Applications: Discover how Satclip can be used in low-resource environments, such as on edge devices, and how it enhances other models by providing geographic context. The Future of GeoAI: Explore the potential future directions for Satclip, including more detailed regional models and the integration of multiple data modalities. Connect with Konstantine https://www.linkedin.com/in/konstantinklemmer/ Try Satclip https://github.com/microsoft/satclip   Recommended Listening https://mapscaping.com/podcast/computer-vision-and-geoai/ https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/

In this episode, I welcome Jason Gilman, a Principal Software Engineer at Element 84, to explore the exciting world of natural language geocoding. Key Topics Discussed: Introduction to Natural Language Geocoding: Jason explains the concept of natural language geocoding and its significance in converting textual descriptions of locations into precise geographical data. This involves using large language models to interpret a user's natural language input, such as "the coast of Florida south of Miami," and transform it into an accurate polygon that represents that specific area on a map. This process automates and simplifies how users interact with geospatial data, making it more accessible and user-friendly. The Evolution of AI and ML in Geospatial Work: Over the last six months, Jason has shifted focus to AI and machine learning, leveraging large language models to enhance geospatial data processing. Challenges and Solutions: Jason discusses the challenges of interpreting natural language descriptions and the solutions they've implemented, such as using JSON schemas and OpenStreetMap data. Applications and Use Cases: From finding specific datasets to processing geographical queries, the applications of natural language geocoding are vast. Jason shares some real-world examples and potential future uses. Future of Geospatial AIML: Jason touches on the broader implications of geospatial AI and ML, including the potential for natural language geoprocessing and its impact on scientific research and everyday applications. Interesting Insights: The use of large language models can simplify complex geospatial queries, making advanced geospatial analysis accessible to non-experts. Integration of AI and machine learning with traditional geospatial tools opens new avenues for research and application, from environmental monitoring to urban planning. Quotes: "Natural language geocoding is about turning a user's textual description of a place on Earth into a precise polygon." "The combination of vision models and large language models allows us to automate complex tasks that previously required manual effort." Additional Resources: Element 84 Website State of the Map US Conference Talk on YouTube Blog Posts on Natural Language Geocoding Connect with Jason: Visit Element 84's website for more information and contact details. Google "Element 84 Natural Language Geocoding" for additional resources and talks.

This podcast episode is all about semantic search and using embeddings to analyse text and social media data. Dominik Weckmüller, a researcher at the Technical University of Dresden, talks about his PhD research, where he looks at how to analyze text with geographic references.  He explains hyperloglog and embeddings, showing how these methods capture the meaning of text and can be used to search big databases without knowing the topics beforehand. Here are the main points discussed: Intro to Semantic Search and Hyperloglog: Looking at social media data by counting different users talking about specific topics in parks, while keeping privacy in mind. Embeddings and Deep Learning Models: Turning text into numerical vectors (embeddings) to understand its meaning, allowing for advanced searches. Application Examples: Using embeddings to search for things like emotions or activities in parks without needing predefined keywords. Creating and Using Embeddings: Tools like transformers.js let you make embeddings on your computer, making it easy to analyze text. Challenges and Innovations: Talking about how to explain the models, deal with long texts, and keep data private when using embeddings. Future Directions: The potential for using embeddings with different media (like images and videos) and languages, plus the ongoing research in this fast-moving field. Connect with Dominik Weckmüller here https://geo.rocks/ Stay up to date with AI here https://huggingface.co/ Try searching for “map”  here https://huggingface.co/spaces   Check out this project I am working on  https://quickmaptools.com/

In this episode, we welcome back Lauren Guy, CEO and founder of ASTERRA, a groundbreaking company using L band and synthetic aperture radar (SAR) for commercial purposes. Lauren shares his journey as a geophysicist and discusses the innovative applications of L band in detecting water leakages, soil moisture, and even minerals from space. Dive deep into the technical, commercial, and environmental aspects of SAR technology and learn about the future potential of this exciting field. **Key Topics Covered:** **Introduction to Astera**:    - Overview of Asterra's mission and Lauren Guy's background as a geophysicist.    - The unique use of L band and SAR for commercial applications.   **Understanding L Band and Synthetic Aperture Radar (SAR)**:    - Explanation of the electromagnetic spectrum and how L band fits in.    - Advantages of L band, including its ability to penetrate the ground.   **Technical Insights into SAR**:    - Detailed discussion on polarizations, signal processing, and the electrical properties of materials detected by SAR.    - Comparison between L band and other bands like X and C band.   **Applications and Benefits of L Band**:    - Real-world examples of how Astera uses L band for water leak detection and soil moisture mapping.    - Discussion on the environmental and commercial impact of these applications.   **Challenges and Limitations**:    - Addressing issues such as noise interference from cell phones and radars.    - Limitations in resolution and the complexities of SAR technology.   **Case Studies and Success Stories**:    - Success stories including the detection of 118,000 water leakages worldwide and the discovery of significant lithium deposits.   **Business Strategies and Market Penetration**:    - Insights into ASTERRA's business model, customer education, and market challenges.    - Strategies for overcoming barriers and building trust with clients.   **Future Aspirations and Technological Developments**:    - Plans for launching their own satellites to ensure reliable data sources.    - The role of AI in enhancing SAR capabilities and improving detection accuracy.   **Entrepreneurial Advice for Remote Sensing Practitioners**:    - Lauren’s advice for remote sensing scientists and entrepreneurs in the industry.    - The importance of data feedback loops and the journey from a 20% to an 86% success rate in detections.   **Guest Information:** - **Lauren Guy**: CTO and founder of ASTERRA. Connect with Lauren on https://www.linkedin.com/in/lauren-guy-asterra/   **Company Information:** - **ASTERRA**: Learn more about ASTERRA’s innovative solutions at https://asterra.io/   **Additional Resources:** - Check out Lauren’s previous appearance on the podcast for more insights into SAR technology. - Explore ASTERRA’s groundbreaking work in remote sensing and their various applications across different industries.   **Episode Highlights:** - "We can find water leakages from space and distinguish treated water from other types of water based on their dielectric properties." - "ASTERRA has verified, dug, and fixed 118,000 leakages across 65 countries using L band SAR technology." - "Our success rate has increased from 20% to around 86% thanks to the integration of AI and continuous data feedback."   **Support the Show:** - If you enjoyed this episode, please leave a review on your favourite podcast platform and share it with your network.   Thank you for tuning in to the MapScaping Podcast!   Recommended Listening  Finding Water Leaks From Space Introduction To Synthetic Aperture Radar-SAR Flood Monitoring From Space ( using SAR)

Cloud-native geospatial, range requests, chucks, COGs and COPCs ... [ insert confusing acronym here ]  Sometimes It feels like we need to learn a whole new vocabulary and if you have been doing #geo for a while you might be wondering how much of this is actually going to impact me. What bits of this are the ones that I need to know about?  I don’t think that anyone is going to be talking about cloud native in 10 years, in the same way, no one talks about digital cartography or computer analysis because where else would you do your cartography? And how else would you do your analysis?  Maybe the names won’t be as important but the concepts will and while this episode is focused on Geoparquet it does so within the context of cloud-native geospatial - and this concept is not going away!    You can connect with Kyle Barron here https://x.com/kylebarron2 or here https://kylebarron.dev/     If you want to learn more about cloud-native geospatial I can highly recommend these episodes   https://mapscaping.com/podcast/cloud-optimized-point-clouds/ https://mapscaping.com/podcast/introduction-to-cloud-native-geospatial/ https://mapscaping.com/podcast/planet-scale-tiled-maps-without-a-server/ https://mapscaping.com/podcast/what-is-modern-gis/ https://mapscaping.com/podcast/the-planetary-computer/   I am working on a new project called https://quickmaptools.com/ like the name suggests it is a bunch of browser-based map tools. So far we have created several different conversion tools and will continue to add more to the list. Check it and let me know what you think!

Mappedin started as a school project and evolved into a leading indoor mapping company, working with malls, airports, hospitals, and Fortune 500 companies.   You guessed, today's podcast is all about indoor mapping, why it's hard, what are the use cases driving it, what the state of the art looks like today and what we can expect in the future.   Key points discussed include: 1. **The Challenge of Indoor Mapping**: Unlike outdoor mapping, indoor environments are complex due to the density of objects and frequent changes. Moreover, indoor spaces are mostly private property, making it difficult to collect data comprehensively. 2. **Importance of Indoor Maps**: Despite the challenges, the need for indoor maps is growing. Applications range from wayfinding in malls and airports to optimizing logistics in warehouses and creating better guest experiences in various venues. 3. **Mappedins's Approach**: enabling non-experts to create and maintain indoor maps. Their tools are designed for everyday users, allowing them to update maps as easily as they would a document in Google Docs. 4. **Technological Advances**: While technologies like LiDAR and digital twins offer detailed 3D models, are they really practical? . 5. **Indoor Positioning**: Accurate indoor positioning is crucial for the success of indoor maps, similar to how GPS revolutionized outdoor mapping. However, this remains a challenging area due to signal interference and the complexity of indoor spaces. 6. **Future Outlook**:  Digital indoor maps become as ubiquitous as Wi-Fi, providing essential data for various applications and improving overall user experience ... but we are not there yet! Try Mappedin for yourself https://www.mappedin.com/ or connect with Hongwei  here  https://www.linkedin.com/in/hongweil/    Recommended Listening  Where does the blue dot come from ( how Google knows your location ) Hyper Accurate Indoor Location Using the Geomagnetic fields of buildings to navigate indoors   I am working on a new project https://quickmaptools.com/ and would really appreciate some feedback!

Hugo Powell, from immap.org shares his expert insights on how GIS technologies are leveraged to analyze data, visualize scenarios, and facilitate rapid decision-making during emergencies. Here are the key tools mentioned: 1. **Kobo Toolbox**: An open-source tool used for data collection in humanitarian contexts. Kobo Toolbox allows for both quantitative and qualitative data collection and is operational offline, which is crucial in areas with limited internet connectivity. It supports geospatial data collection and can be used for needs assessments in settings like refugee camps. 2. **ODK Collect**: Similar to Kobo Toolbox, ODK Collect is an open-source mobile application used for field data collection. It is widely used in humanitarian efforts for its ease of use and the capability to work offline. 3. **QGIS**: A free and open-source geographic information system used for viewing, editing, and analyzing geospatial data. Hugo notes that QGIS is core for mapping and data analysis in humanitarian operations. 4. **Tableau and Power BI**: Business intelligence tools mentioned for their use in analyzing and visualizing data. These tools help in making data-driven decisions during humanitarian operations. 5. **Humanitarian Data Exchange (HDX)**: An open platform for sharing data across crises and organizations, which helps in avoiding duplication of efforts and enhances coordination among humanitarian actors. 6. **Humanitarian OpenStreetMap Team (HOT)**: Provides crowdsourced geospatial data which is extremely valuable in humanitarian settings for its accuracy and timeliness. 7. **Esri’s Living Atlas and other Esri tools**: While not open-source, Esri’s tools are sometimes used for their comprehensive geospatial data, particularly in natural disaster contexts like earthquakes. 8. **Humanitarian Spatial Data Center**: Managed by IMAP, this tool aggregates and processes data, providing access to data, analytics, and visualization tools all in one place. It has been particularly successful in deployments like Afghanistan.   This episode was sponsored by scribblemaps.com https://youtu.be/CDkG9eS6H2M   Recommended Listening  Geospatial Support For Humanitarian Emergencies A Self-Contained Environment For Open-Source Geospatial Tools GGIS Offline And In The Field The Business Of Web Maps Peer to Peer Mapping And Digital Democracy   I am working on a new project over at QuickMapTools.com and any feedback is really appreciated!

AI Autocomplete for QGIS Brendan Ashworth the CTO and co-founder of https://buntinglabs.com/ focuses on integrating AI with QGIS, and today on the podcast we are talking about Autocomplete for vectorization. Along the way Brendan will share with us why Bunting Labs chose to build this on top of QGIS, the Challenges in Map Digitization, what the development process was like and how this is different from tools like Segment Anything ( from meta )  Here's what we discussed: Introduction to Bunting Labs: Get to know more about Brendan and Bunting Labs, whose mission revolves around enhancing QGIS with AI, especially focusing on automating vectorization processes. AI Autocomplete for Vectorization: We explored the AI autocomplete feature developed by Bunting Labs that simplifies the vectorization of maps in QGIS, streamlining the digitization process for better efficiency. Brendan’s Background and Motivation: Brendan shared his journey from a software engineer to a pivotal player in the geospatial sector, spurred by a project that showcased the potential of merging geospatial data with machine learning. Why Choose QGIS?: Discover why Bunting Labs opted for QGIS over other GIS platforms, with an emphasis on its open-source nature and vibrant community ecosystem. Challenges in Map Digitization: Our conversation covered the technical challenges involved in developing AI capable of accurately understanding and digitizing maps. Iterative Development and Learning: Brendan highlighted the evolutionary process of their AI model, which has significantly improved from its early versions. AI vs. Segment Anything: Brendan explained how their AI autocomplete tool differs from existing solutions like Segment Anything, particularly in handling specific digitizing challenges. The Future of AI in Geospatial Data Analysis: We discussed potential future applications of AI in geospatial data, including automatic georeferencing and metadata extraction. Privacy Considerations: We also touched on the importance of privacy in the development and deployment of AI technologies in geospatial data analysis. Changing the Geospatial Landscape: Brendan shared his vision for using geospatial data not just to map the current world but to plan and improve future landscapes. Sponsored by https://www.scribblemaps.com/ Recommended Listening https://mapscaping.com/podcast/the-business-of-web-maps/ https://mapscaping.com/podcast/the-business-of-qgis-development/ https://mapscaping.com/podcast/qgis-offline-and-in-the-field/ https://mapscaping.com/podcast/computer-vision-and-geoai/   https://quickmaptools.com/ - MapTools to save your time processing GIS data

GNSS receivers - why precise positioning will not be coming to your phone any time soon   Igor is the CEO and cofounder of Emlid.com a company that started out making high-precision GNSS receivers in his kitchen and crowd-funded the first batch on Kickstarter.    But that was over ten years ago so today on the podcast Igor is going to tell us about the innovations that made this possible, give a great explanation of RTK and PPP and explain why we should expect to see high precision positioning on your phone any time soon. Connect with Igor here: https://www.linkedin.com/in/igor-vereninov-52a73ab0/ Or visit https://emlid.com/   In this episode, we cover:  **Introduction to Emlid and its Focus**: Introduction to the company Emlid, its CEO and co-founder Igor, and their focus on high precision GNSS receivers and software designed for centimeter accuracy positioning.   **Startup Story and Crowdfunding**: The origin story of Emlid, starting from working with drones in university, the need for accurate maps, the initial challenges with high precision GPS technology, the development of their own GNSS receiver, and their successful crowdfunding campaign on Kickstarter.   **Innovations in GNSS Technology**: How Emlid managed to make high precision GNSS technology more affordable and accessible, the role of open-source software, and the technical innovations that allowed them to reduce the cost and size of GNSS receivers.   **Market and Technology Evolution**: The discussion on how the market for GNSS technology has evolved, including the impact of autonomous cars on the development and availability of multi-frequency GNSS chips, and how these advancements benefited broader applications beyond surveying and construction.   **Precision vs. Accuracy in GNSS**: An explanation of the difference between precision and accuracy in the context of GNSS technology, and the significance of each in various applications like drone mapping and volume measurements.   **RTK and PPP Explained**: A detailed explanation of Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), including how they work, their applications, and their advantages and limitations.   **The Future of GNSS Technology**: Insights into the future directions of GNSS technology, including the challenges and potentials for achieving sub-centimeter accuracy with smartphones, the complementarity of GNSS and visual positioning systems, and the potential for GNSS technology to replace traditional surveying methods.   **Global Utility of GNSS**: A discussion on the importance of GNSS as a global utility, its indispensable role in modern technology and everyday life, and the potential consequences of GNSS failures. More GNSS-related podcast episodes! From GNSS to VPS Reimaging GPS How Google Knows Your Location Past, Present and Future of GNSS SBAS - A base station in the sky

Refactoring the Way you Talk About your geospatial skills: It is Costing you Money Some of the key topics in this episode  1.Our Geospatial Skills and Marketability: There's a realization that while our traditional geospatial skills are valuable, they might not always be marketed effectively to match the broader IT and data analysis job markets. We discuss the benefit of framing our skills in terms that are more widely recognized outside the niche of geospatial technology, such as data science or IT. 2.The Spatial Discount: We explore the concept of the spatial discount, which refers to the observation that geospatial professionals might face a disparity in compensation compared to their counterparts in more generalized IT roles, despite having highly transferable and valuable data manipulation skills. 3. Skill Development and Adaptation: The importance of continually developing skills that are not only advanced within the geospatial domain but also marketable across various sectors is emphasized. Learning and mastering technologies that have broad applications, such as SQL for spatial data manipulation, can enhance our versatility and marketability. 4. Communication and Marketing Skills: Our ability to effectively communicate and market our skills is highlighted as crucial for career advancement. We are encouraged to adopt the language and terminology that resonate with broader industries and potential employers, moving beyond the jargon of the geospatial field. 5. Finding Value in Our Geospatial Work: The discussion also touches on the importance of identifying and articulating the real-world value of our geospatial work. We should focus on how our skills can solve practical problems and address the needs of businesses and organizations, rather than solely on the technical complexity of our tasks. 6. Professional Development: Lastly, the conversation advocates for a proactive approach to our professional development, suggesting that we should seek out opportunities to learn new skills and technologies that align with market demands and personal interests. These points collectively suggest a strategy for us, as geospatial professionals, to enhance our career prospects: by broadening our skill sets, effectively marketing our capabilities, and aligning our work with the needs and language of the wider IT and data analysis fields.   Connect with Brain Timoney on LinkedIn  Thank you to our sponsors  https://www.scribblemaps.com/  https://merginmaps.com/   Recommended Listening   Modern Geospatial Rebranding Gis and Geospatial Getting Where You Want To Go In Your Geospatial Career Mid-Career Change

Modern geospatial - not the bleeding edge of geospatial but modern geospatial - what is it? Well my guest Will Cadell, the CEO of SparkGeo describes modern geospatial as the intersection of the cloud, smart space, open source data/standards, AI and smart devices - that's modern geospatial  And as you will hear during the discussion it's important to understand the difference between modernisation and innovation when we think about moving people from where they are now to where they want to be with regards to their geospatial capabilities.  You might be wondering - what does any of this have to do with me? I just want to make better things, I just want to help people use all this awesome geospatial stuff … but you don’t get to do that without first understanding what “better” looks like for them - what is their version of awesome geo stuff … and that is why you should listen to this episode!    Connect with Will Cadell Twitter https://twitter.com/geo_will LinkedIn https://www.linkedin.com/in/willcadell/ SparkGeo  https://sparkgeo.com/ https://www.strategicgeospatial.com/   This episode is sponsored by https://www.scribblemaps.com/   Recommended Listening   The Business of Web Maps https://mapscaping.com/podcast/the-business-of-web-maps/   Modern GIS https://mapscaping.com/podcast/what-is-modern-gis/

The main topics discussed during this episode include: Basics of LIDAR data and its applications. Differences between LIDAR and photogrammetry. Processing chain of LIDAR data. Challenges in classifying point clouds. Applications of LIDAR technology in vegetation mapping, terrain modelling, and infrastructure inspection. The future of LIDAR technology includes the potential for more affordable and widespread use. Importance of automated processing tools for handling large volumes of data. Connect with Nejc Dougan here: https://www.linkedin.com/in/nejcdougan/ or at https://www.flai.ai/    Recommended listening    Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/   PDAL - the point data abstraction library https://mapscaping.com/podcast/pdal-point-data-abstraction-library/   Lidar from drones https://mapscaping.com/podcast/lidar-from-drones/   Bathymetric Lidar https://mapscaping.com/podcast/bathymetric-lidar-and-blue-carbon/

Alex Leith is a Digital Earth Architect and in this episode, you will learn what Infrastructure as code is  - hint it is the opposite of the "clicky-clicky" and so much more! Connect with Alex here https://auspatious.com/   Recommended Listening  Cloud-Optimized Point CLounds https://mapscaping.com/podcast/cloud-optimized-point-clouds/   Cloud Native Geospatial  https://mapscaping.com/podcast/cloud-native-geospatial/   Planet Scale Tiled Maps without a Server https://mapscaping.com/podcast/planet-scale-tiled-maps-without-a-server/   What is Modern GIS https://mapscaping.com/podcast/what-is-modern-gis/

GeeMap is an open-source Python library that provides tools for interactive mapping with Google Earth Engine (GEE), which is a platform for earth science data and analysis ... and today you are going to hear from the creator of GeeMap!   Connect with Qiusheng Wu here: https://wetlands.io/   This episode is sponsored by Planet learn more at https://www.planet.com/gis/   Recommended Listening Introduction to Google Earth Engine https://mapscaping.com/podcast/introducing-google-earth-engine/   Introduction to Sentinel Hub https://mapscaping.com/podcast/sentinel-hub/   Planet - Imaging everything every day ( almost )  https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/   Introduction to Microsoft's Planetary Computer https://mapscaping.com/podcast/the-planetary-computer/

GPS reimagined? Why do we need to reimagine GPS? ... Is it broken?   Recommended Podcast Episodes   How Google Calculates Your Location https://mapscaping.com/podcast/how-google-calculates-your-location/   From GNSS To VPS https://mapscaping.com/podcast/from-gnss-to-vps/   Navigating The Past Present and Future of GNSS https://mapscaping.com/podcast/navigating-the-past-present-and-future-of-gnss/   SatelliteBased Augmentation System - A Base Station In The Sky https://mapscaping.com/podcast/satellite-based-augmentation-system-a-base-station-in-the-sky/

Nyall Dawson is a QGIS developer, cartographer, and owner and founder of North Road, a company specializing in open-source geospatial software. His journey into geospatial began with personal interests in mapping and cartography, which later evolved into a business called North Road. But that's not why I wanted to make this episode for you, I wanted to share this story with you because it could be your story too.  You could decide to have a story that starts with contributing to something you care about, which leads to you becoming a known expert within a community that cares about the same thing and evolves into paid opportunities. That could be your story too!   You can connect with Nyall here: https://twitter.com/nyalldawson https://www.linkedin.com/in/nyall-dawson-18b6016a/   Sponsored by Planet  Learn more at www.planet.com/gis           Recommended Podcast Episodes   Planet https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/   Monetizing an open-source geospatial project https://mapscaping.com/podcast/monetizing-an-open-source-geospatial-project/   Being self-employed in Earth Observation https://mapscaping.com/podcast/being-self-employed-in-the-earth-observation-sector/   Geospatial Side Hustles https://mapscaping.com/podcast/geospatial-side-hustles/   Self Employment in the GIS Industry https://mapscaping.com/podcast/self-employment-in-the-gis-geospatial-industry/   A Business built on Open Source GIS https://mapscaping.com/podcast/a-business-built-on-open-source-gis/

This episode is all about making beautiful maps ... I am not a cartographer but my guest Mamata Akella is a professional cartographer at Felt!  So today on the podcast we are talking about Essential Elements of Map Design: Which of course starts with questions like - who is it for, what is it for and how do we get it to them? And then moves on to Visual Hierarchy,   Zoom-Based Styling, Color Palettes, and Interpretation We discuss a few practical examples during the conversation and you can find links to those in the show notes    Recommended Listening https://mapscaping.com/podcast/felt-upload-anything/ https://mapscaping.com/podcast/communicating-with-maps-the-art-of-cartography/ https://mapscaping.com/podcast/full-stack-cartography/

Planet manufactures and manages the world’s largest constellation of earth observation satellites! Imaging “Just about everywhere on earth just about every day – Making change visible, accessible, and actionable” … and the hope of this episode is to help you understand how they do that – along the way you will hear about their two constellations and how they work together Learn the difference between ghost ships and dark ships and find out that there are very few ground control points in the ocean and why that matters Find out what this means for GIS and permit enforcement. For more information go to https://www.planet.com/gis/   Recommended Podcast Episodes   Hyperspectral vs. Multispectral https://mapscaping.com/podcast/hyperspectral-vs-multispectral/ NICFI Program https://mapscaping.com/podcast/reduce-and-reverse-tropical-forest-loss-with-nicfi/ Synthetic Data https://mapscaping.com/podcast/synthetic-data-for-real-problems/ Labels Matter https://mapscaping.com/podcast/labels-matter/

This episode is a story about wide-angle imaging for fire mapping and maritime search but it's also a story about changing the culture and getting people to trust a new way of doing things.   My guest today is Alison Harrod - mission success manager at a start-up called Overwatch imaging    Whenever I work with a company like Overwatch Imaging it is hard to know which story to tell, we could just as easily have made an episode about AI and object detection or about smart sensors because they do those things too.     The decision depends on the guest and their background so after meeting Alison we decided to make this episode for you and try to give you a broad overview of what wide-angle imaging is and how it's used in the context of fire mapping and maritime search. ...but It's one thing to have a technology and it is another thing entirely to get people to use it … as you will hear fire mapping is not “a move fast and break things kind of situation”   Connect with Alison here! https://www.linkedin.com/in/alisonharrod/ https://www.overwatchimaging.com/   Other relevant podcast episodes that you might enjoy Thermal Imagery From Space https://mapscaping.com/podcast/thermal-imagery-from-space/ Finding Water Leaks From Space https://mapscaping.com/podcast/finding-water-leaks-from-space/ Cube Satellites Of The Stratosphere https://mapscaping.com/podcast/cube-satellites-of-the-stratosphere/

It's not about becoming an influencer it's about creating opportunities for yourself   In this episode, we tackle the common misconception that personal branding is solely for influencers, revealing how it's actually about creating the right visibility and opportunities in your professional sphere. Helena Merschdorf shares her unique insights, drawing from her rich background in GIS and marketing, and discusses: Solving the Obscurity Problem: Discover how personal branding can help you get noticed by the right people, not just everyone. Effective Communication in Technical Fields: Learn the art of conveying complex GIS concepts to non-experts. Defining and Building Your Personal Brand: Uncover the essence of personal branding and how to strategically develop it. Overcoming Challenges: Helena offers guidance on tackling imposter syndrome and finding your niche in the vast world of GIS. Choosing the Right Platforms: Get tips on selecting the best channels for your personal branding efforts based on your target audience. Success Stories and Strategies: While specific examples aren't named, learn about the different levels of personal branding success and what might work for you. This episode is not just about building a personal brand; it's about leveraging that brand to carve a unique path in the geospatial industry.   Connect with Helena here https://www.linkedin.com/in/helenamerschdorf/ https://www.tales.co.nz/   Other relevant podcast episodes  Rebranding GIS and Geospatial https://mapscaping.com/podcast/rebranding-gis-geospatial/   Python Maps https://mapscaping.com/podcast/python-maps/   Getting Your Dream Job In Earth Observation https://mapscaping.com/podcast/getting-your-dream-job-in-earth-observation/

Entity resolution is the process of matching and merging records from different sources that refer to the same entity.   today's episode is about entity resolution for place data, why you might want to do that, and what any of this has to do with the dollar, Unix time and the idea that If data is really driving innovation, join keys are going to become more valuable.   Today's guest is Auren Hoffman https://www.linkedin.com/in/auren/ https://twitter.com/auren https://www.youtube.com/@worldofdaas   If you want to try Placekey for yourself go to https://www.placekey.io/   If you want to learn more about SafeGraph listen to this podcast episode https://mapscaping.com/podcast/building-geospatial-truth-sets/

Embracing Open-Source Geospatial Technology is easy as an individual but what if you want your organization to use FOSS4G How do you get strategic buy-in? It turns out that the software does not sell itself and that even in the age of AI we still have to convince a human if we want organizational change to to happen.  I think the temptation is to say hey look at this long list of specifications and notice how FOSS4G is often better or equal to the close source equivalent.  Or hey look at the price tag … it costs nothing which is way cheaper than this other thing which costs more than nothing. While this might be all the argumentation you need in some cases … in general, making change happen is hard,  and it's going to require more than that.  That's why I have invited Todd Barr back on the podcast to walk us through what it takes to get an organization to Embrace Open-Source Geospatial Technology.   Here are a few of the key points Getting buy-in for open-source software and addressing concerns about security and IP protection Perspectives of External and internal stakeholders on open-source Software Importance of collaboration, empathy, and understanding in decision-making and stakeholder management Challenges of implementing open source technology in a corporate environment Benefits of using open source solutions, such as faster analysis, increased stability, and flexibility for innovation Accessibility and support in open source communities, including direct interaction with developers and availability of external consultants Customization and development work required for creating vertical solutions with open-source components Finding skilled developers and training them in geospatial technology Cost-saving advantages of open source technology in cloud computing Leading arguments for implementing open source software: cost savings and freedom to modify and customize Advocacy for supporting and integrating with the open source community. The last time Todd was on the podcast we talked about Leadership and Mentorship in the Geospatial community https://mapscaping.com/podcast/skills-leadership-mentorship-and-the-geospatial-community/   If you are interested in FOSS4G you might enjoy these previous episodes   A Business built on Open Source GIS https://mapscaping.com/podcast/a-business-built-on-open-source-gis/   Monetizing an open-source geospatial project https://mapscaping.com/podcast/monetizing-an-open-source-geospatial-project/   Or just scroll through the archive to find episodes about QGIS, PostGIS, Geoserver, Geonode, Python, and a bunch of other open-source projects   I could use some support! please consider supporting this podcast on Patreon https://www.patreon.com/MapScaping    Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Republished Episode - I wanted to resurface this episode because positioning is everything, and it's a great overview of how it works.   Right now you are being bathed in radio signals from about 35 satellites, each one 20,000 km away, and every one of them is weaker than the noise floor of the receiver trying to hear it. That is GNSS, and it is the only technology on Earth that can tell you where you are in an absolute sense. Sandy Kennedy runs applied research for autonomy and positioning at Hexagon. In this conversation she walks through how satellite positioning actually works, why centimetre accuracy is so hard to get, and what the next generation of positioning looks like. We cover low earth orbit constellations, visual positioning, Wi-Fi, 5G and ultra wideband, and the one thing every one of those systems still quietly borrows from GPS. If you have ever wondered whether visual positioning is going to replace GNSS, or why nobody has just put GPS satellites in low earth orbit already, this one is for you. Show notes Sandy Kennedy is the Vice President of Innovation for Autonomy and Positioning at Hexagon. Her group does applied research: finding better ways to do what Hexagon's positioning products already do, and finding new things they could do in the future. This episode is a tour of positioning from the top down. We start 20,000 km up with the GNSS constellations, look at what a low earth orbit constellation would change, then come back down to Earth for visual positioning, Wi-Fi, 5G, ultra wideband and the private networks used in mines and warehouses. The through line is that none of these technologies replaces the others. They each have environments where they are strong and environments where they fall over, and the real work ahead is making them hand off to each other seamlessly. What "better positioning" actually meansSandy's old grad supervisor used to ask "better in which parameter?" Better depends on the job. For positioning it usually comes down to availability (how often you can get a fix), whether that fix is accurate enough to be useful, and then accessibility: can the receiver be made small enough, cheap enough and cool enough to go where it is needed. Power is not just power, it is also heat. How GNSS worksEvery constellation (GPS, Galileo, BeiDou, GLONASS) is a state-owned, state-operated set of synchronised satellites in medium earth orbit. Your receiver measures the time a signal took to arrive, multiplies by the speed of light, and gets a distance. Four satellites give you four unknowns: X, Y, Z and your clock offset from system time. More satellites give you redundancy, and in this case redundancy is a good thing. In open sky today a receiver can see about 35 satellites at once, each broadcasting on around three frequencies. Why centimetre accuracy is hard- The signal is below the noise floor of your receiver. There nto. You have to fish it out.- Broadcast orbits are only accurate to metres. That is remarkable for something 20,000 km away, but if you want centimetres you need precise orbits from  a correction service.- The troposphere delays the signal and changes with water vapour. The ionosphere is dispersive and tears code and carrier apart, and it follows the solar  cycle. Multi-frequency receivers can observe and remove mostally.- Multipath. In a prairie there is nothing to bounce off. Over water there is more. In a city you are surrounded by hard metal and stone, and the receiver  has to work out which arrival was the direct line of sight. Why not just put GNSS satellites in low earth orbit?Daniel pitches it as a startup idea and Sandy takes it apart, fairly. LEO is cheaper to launch to, satellites need less radiation hardening, the signalarrives stronger and cuts through foliage better (not buildingte slices through the atmosphere in a way that helps separateorbital, atmospheric and multipath errors quickly. LEO satellites can also position themselves using the existing GNSS cons

In this podcast episode, Jennings Anderson, a research scientist at Meta, discusses the Overture Maps Foundation, a downstream product of OpenStreetMap. He explains his background in open map data and his interest in studying collaboration within the OpenStreetMap community. Jennings then dives into the Daylight Distribution, an open data product produced by Meta, and how it combines building data sets from various sources into one unified theme. Jennings emphasizes the importance of a stable ID system within the Overture Maps Foundation and the potential for easy conflation and integration of third-party data. Jennings also explains the relationship between OpenStreetMap and Overture Maps, highlighting how they complement each other.   Relevant podcast episodes  OpenStreetMap Is A Community Of Communities Cloud Native Geospatial Cloud Optimized Point Clouds The Rapid Editor With regards to accessing Overture Map data, you might find this YouTube video helpful https://youtu.be/fZj6kTwXN1U?feature=shared   Just in case you are interested in the Google building footprints here is a link to that :)  https://sites.research.google/open-buildings/   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

100 billion Points Every Day 100 billion is a very large number, let's say that I gave you a spreadsheet with 100 billion rows in it, each row consisted of five columns Latitude, Longitude, Device ID, A Timestamp, and a column telling the name of the data provider What would you do with that?  How would you clean it? Make sense of it? Extract value from it? What would people use it for? And how would you do this in a way that could be systematized? FourSquare does this every day with the help of something they call a movement engine. To help understand more about how they do this I have invited Gabriel Durkin the director of data science on the podcast. This is the last in a series of episodes I have worked on together with FourSquare and I have to say it's been really enjoyable working with them.  If you are interested in hearing some of the previous episodes just check out the links below!   From Pixels To Patterns AI In Spatial Analysis https://mapscaping.com/podcast/from-pixels-to-patterns-ai-in-spatial-analysis/ Big Data In The Browser https://mapscaping.com/podcast/big-data-in-the-browser/ Spatial Knowledge Graphs https://mapscaping.com/podcast/spatial-knowledge-graphs/ Designing For Location Privacy https://mapscaping.com/podcast/designing-for-location-privacy/ All Of The Places In The World https://mapscaping.com/podcast/all-of-the-places-in-the-world/ Geospatial Jobs   There are a few new jobs on our Job Board! The most interesting one is the role of Social Media Manager at Felt - United States (Remote)  ( If you want to apply for this one, it might be a good idea to listen to this episode first ;)  https://mapscaping.com/podcast/felt-upload-anything/ ) See more at https://mapscaping.com/jobs/   As a bonus for reading all the way to the end :)    If you are looking for free terrain data for anywhere in the world you might find this useful  https://github.com/openterrain/openterrain/wiki/Terrain-Data Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Computer vision is everywhere! But teaching an algorithm to identify objects requires a lot of data and this is definitely the case when we think about GeoAI   But it is not enough to have a lot of data we also need data that is labeled If we are looking for cars in images we need a lot of images of cars and we need to know which pixels are the car!  Of course, I am oversimplifying but I hope you get the idea,  Now imagine that you can automatically generate a large labeled data set of realistic images of cars based on the specifications of a specific sensor. These data sets are often referred to as synthetic data or fake data and to help us understand more about this I have invited Chris Andrews from Rendered AI on the podcast.   Here are a few previous episodes you might find interesting    Computer Vision And GeoAI https://mapscaping.com/podcast/computer-vision-and-geoai/ In this episode, the discussion is aimed at an increased understanding of the differences between computer vision and the AI that is used in the Earth Observation world.   Labels Matter https://mapscaping.com/podcast/labels-matter/ What it takes to create labeled training data manually. If you are new to the idea of labeled data sets this is a good place to start.   Fake Satellite Imagery https://mapscaping.com/podcast/fake-satellite-imagery/ This is a good episode if you want to know more about Generative AI and Generative Adversarial Networks.    Also, check out this website https://thisxdoesnotexist.com/ to get an idea of where and how these Generative Adversarial Networks can be used. Look for a website called This City Does Not Exist  http://thiscitydoesnotexist.com/   On a silently similar note try uploading an image to https://bard.google.com/ … it's pretty interesting!

This is an interview with a senior data scientist from Hub Ocean, a platform that aims to unlock and unite ocean data. Hub Ocean - as the name suggests is a hub for ocean data  Now we have talked about these kinds of data hubs before on the podcast - Sentinal Hub - a data hub for earth observation data, Microsoft Planetary Computer, Google Earth Engine, Open Topography is data but for topography data …. The concept is not new but also not easy to implement and if they work, these types of data hubs have a gravity to them that becomes more powerful over time. One of the guiding concepts behind these data hubs seems to be the idea of FAIR data - Findable, Accessible, Interoperable, and Reuseable data   …. But its not enough to ensure that the data is fair I think we should also consider how we can make the results of our research Findable, Accessible, Interoperable, and Reuseable data  If you are not already familiar with Cloud Optimised Geospatial formats it is worth checking out these two episodes.  https://mapscaping.com/podcast/cloud-optimized-point-clouds/ https://mapscaping.com/podcast/cloud-native-geospatial/   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

felt.com is a browser-based mapping tool and its also a reminder that just because we have always done web mapping one way it doesn’t mean it always has to be done that way. For example, Felt lets you upload anything! That's a bold promise, you can upload anything you want and we will figure it out on the back end.  Felt is also the first and only flagship sustaining member of the QGIS project, they are supporting the development of an open-source tiling engine, called Tippecanoe. They also support protomaps and the development of PMtiles as well as contributing code to Maplibre and Gdal    ... But that is not why you should listen to this episode … you should listen to this episode because if we are going to grow the geo pie we need more upload anything buttons   Easily bring data into Felt with our QGIS plug-in and Felt API  How We Make Your Data Look Great at Every Scale with Tippecanoe   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Rapid is a free open-source web-based editor for an OpenStreetMap. In the past the focus was on conflating AI-generated datasets with OpenStreetMap data but the future for this editor is conflating authoritative datasets with OpenStreetMap.  Humans are in the loop, people reviewing data authoritative datasets and adding them to OpenStreetMap with a few clicks!  So you might be wondering, what is Authoritative data? And perhaps it doesn’t even matter what authoritative means maybe the most important thing is it correct. If you are interested in OpenStreetMap you might enjoy this episode  https://mapscaping.com/podcast/openstreetmap-is-a-community-of-communities/ which is a great introduction to OpenStreetMap as a project but also explains some of the commercial interest in updating the map which adds a lot of context to Rapid and its development and future.   Humanitarian OpenStreetMap Team If you have not heard of Humanitarian OpenStreetMap Team this is well worth checking out!  https://www.hotosm.org/   Segment Anything  (SAM) can segment objects by simply clicking or interactively selecting points to include or exclude from the object. This makes it a user-friendly tool for image segmentation. https://segment-anything.com/   Mapillary Mapillary is a platform that provides street-level imagery and map data from all over the world. The platform is powered by collaboration and computer vision, which helps in generating and maintaining up-to-date, detailed maps. https://mapscaping.com/podcast/scaling-map-data-generation-using-computer-vision/   GeoSpatial Jobs! Drone Deploy’s Earthworks team is looking for an experienced Back End Engineer Full time / Remote NV5 is looking for a Senior GIS Analyst https://mapscaping.com/jobs/ Want to help? I could really use some support!   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

The promise of digital mapping is to provide a shared and real-time view of the state of the underlying system. pg_eventserv is a free and open-source component that helps fulfill the promise of real-time event modeling and shared views in PostgreSQL. By connecting to PostgreSQL and listening on specified channels, pg_eventserv captures database notifications and forwards them to web clients, enabling real-time updates and synchronization of data displayed on maps or other web interfaces. pg_eventserv does one thing and one thing only: take events generated by the PostgreSQL NOTIFY command and passes the payload along to waiting WebSockets clients. pg_eventserv is free and easy to install and you can find it here: https://github.com/CrunchyData/pg_eventserv What this means is that any client can watch for notifications and update as changes in the database happen. Real-time data! Here is a link to a Youtube video demonstration of pg_eventserv in action! https://youtu.be/UakRtYmoWow  I will let Paul Ramsey the creator of pg_eventserv explain all this in more detail in this episode.  If you want to reach out to Paul the best place to do that is http://blog.cleverelephant.ca/  Or if you want to listen to previous episodes with Paul you might find these interesting Raster in the database? https://mapscaping.com/podcast/rasters-in-a-database/ Dynamic Vector Tiles Straight From The Database https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/ Spatial SQL- GIS Without The GIS https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/   also ...  If you are interested in spatial databases at scale ... you might find this episode interesting https://mapscaping.com/podcast/distributing-geospatial-data/     Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

You guessed it this episode is all about recruitment! Who is this episode for? Well if you have a career in geospatial … it's for you!   Getting the job you want, changing your career path, or deciding whether to work at a start-up or a more established company. Once you have made these decisions, how do you stand out from the crowd? What is it the recruiters are looking for? And if you do get offered a contract should you negotiate and what is negotiable?    Jett Metcalf has worked as a geospatial recruitment specialist for a few well-known companies like Google, Descartes Labs and is currently the Head of Talent at Regrow Ag. In this episode, he is going to help us understand some of the answers to these questions.   Reach out to Jett on LinkedIn or Twitter  If you enjoy this episode and are interested in more career-focused episodes go to https://mapscaping.com/podcasts/ and filter by “Geospatial Career"   See you next week!   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

How do we get data from a satellite down to Earth? How do we task a satellite?  Today the answer is likely to be via radios and a system of downlink sites or ground stations. As the satellites pass overhead or within “line of sight” data can be sent via radio from the satellite to the receiver on the ground.  If you don’t want to wait until the satellite can see the ground station, you can send your data to a geostationary satellite that can always see a ground station and let it send the data back to Earth.  Radios are tried and tested, they have been used for this purpose since the inception of satellite communication and radio waves can pass through Earth's atmosphere without significant loss! But … the frequency spectrum for radio waves is strictly regulated, which can limit available channels for communication, and the bandwidth of radio frequencies is limited, which can reduce the volume of data transmission. What about lasers?  You can send more data faster with a laser, you don’t need to worry about interfering with someone else part of the radio spectrum, and ground stations can be much smaller even human-portable!  But … lasers struggle with clouds and the technology is still relatively new So what is the best way to communicate with satellites? Radio or Laser? The answer is … it depends ;)    Jordan Wachs, Director of Business Development for SpaceRake.net does a great job adding context to this discussion but perhaps the bigger question here is what will we do when satellites become internet devices, part of the Internet of Things? What if they were always on always connected in the same way your phone is always on, always connected? What will this enable?    This episode was sponsored by Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing   People who liked this episode also liked …   How to keep your satellite pointing at earth https://mapscaping.com/podcast/how-to-keep-your-satellite-pointing-at-earth/   Hyperspectral v’s Multispectral https://mapscaping.com/podcast/hyperspectral-vs-multispectral/   Sentinel Hub https://mapscaping.com/podcast/sentinel-hub/   Swing by our website sometime https://mapscaping.com/   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

There is a general understanding that it is becoming increasingly difficult to extract meaning from all the data we are collecting without using AI.   But what is AI, and how did we end up in a situation where it is identifying wolves from dogs based on the presence of snow in the background of images?    What does this mean for spatial analysis using tabular data?    What is explainability?    This is not a "how-to" do spatial analysis using an AI episode, it is an overview of  AI in spatial analysis episode with Vin Sharma, VP of Engineering at FourSquare    https://www.linkedin.com/in/ciphr/ https://foursquare.com/

PYGEOAPI is a Python server implementation of the OGC API suite of standards ... which might be really useful if you are thinking about upgrading from the first-generation OGC standards to the second-generation OGC standards  ... or if need to implement a custom data source or custom functionality to your web services.    https://pygeoapi.io   If you are using MapServer, Geoserver, Mapproxy, QGIS server, or Deegree you might find this episode interesting!   Relevant previous episodes   Cloud-native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/   Geoserver https://mapscaping.com/podcast/geoserver/   Geonode https://mapscaping.com/podcast/geonode-open-source-geospatial-content-management-system/   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

So why would anyone want to put alot of data into a browser? Well, for a lot of the same reasons that edge computing and distributed computing have become so popular. You get the data a lot closer to the user and you don’t have to pay for the compute ;)  … this sounds great but as I found out during this conversation it's not as easy as it might seem!  There are a lot of trade-offs that need to be evaluated when moving data and analytics to the client.   Nick Rabinowitz  Senior Staff Software Engineer at Foursquare has a ton of experience with this so he volunteered his time to help us understand more about it. https://location.foursquare.com/ https://studio.foursquare.com/home If you are not familiar with the Arrow data format it might be worth checking out   Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead   Related podcast episodes that you might find interesting include H3 grid system https://mapscaping.com/podcast/h3-geospatial-indexing-system/ The H3 geospatial indexing system is a discrete global grid system consisting of a multi-precision hexagonal tiling of the sphere with hierarchical indexes. H3 is a really interesting approach to tiling data that was developed by UBER and has been open-sourced.  Hex Tiles https://mapscaping.com/podcast/hex-tiles/ If you have not heard of the H3 grid system before listen to that episode first before listening to this one it will add a lot of useful context! Spatial Knowledge Graphs https://mapscaping.com/podcast/spatial-knowledge-graphs/ Foursquare is moving away from spatial joins and focusing on building a knowledge graph. If you are not familiar with graphs this might be a good place to start, also its interesting to hear the reasons for the move from spatial joins to another data structure.   Distribution Geospatial Data https://mapscaping.com/podcast/distributing-geospatial-data/ This is interesting if you want to understand more about distributed databases and some of the strategies for doing this. It sounds complicated but this episode is a really good introduction!    Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ This episode give a solid overview of what cloud-native means and some of the current geospatial cloud native formats out there today   I am constantly thinking about how I can make this podcast better for you so if you have any ideas or suggestions please let me know!  Also, I am thinking of recording a behind-the-scenes episode, is that something you might be interested in? if so what questions do you have?    Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Sounds like a great idea right?   In this episode, Paul Ramsey explains why you shouldn't ... unless you want to ... and how you can ... if you have to.   You can find Paul's blog here: http://blog.cleverelephant.ca/about   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping   Previous episodes with Paul  Spatial SQL https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/   GDAL https://mapscaping.com/podcast/gdal-geospatial-data-abstraction-library/   Dynamic Vector Tiles https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/   Blog posts by Paul about Rasters in the Database https://www.crunchydata.com/blog/postgres-raster-query-basics https://www.crunchydata.com/blog/waiting-for-postgis-3.2-secure-cloud-raster-access   Check Out Our Geospatial Job Board! https://mapscaping.com/jobs/

I am sure you have heard of ChatGPT by now so the hope of this episode is to give you some more context about what is it built on and how it works.   To do that I invited Daniel Whitneck back on the podcast  You can connect with Daniel here https://datadan.io/   and listen to his previous episode here: https://mapscaping.com/podcast/an-introduction-to-artificial-intelligence/   This is perhaps the quote for the episode that I have spent the most time thinking about "We always thought AI would be logical and lack creativity - but it is almost the exact opposite" This reframes the idea of being wrong to being creative which I think you could argue really depends on the context!    If you have not already played around with ChatGPT it's well worth spending the time to experiment with it ... while its still free ;)  https://chat.openai.com/auth/login   Further listening    If you have not already listened to this episode about computer vision and GeoAI you might find it interesting. Listen out for the discussion around plausible / realistic data and real measurements - I think this gives more context to the use cases for generative AI  https://mapscaping.com/podcast/computer-vision-and-geoai/   You might also enjoy this episode about fake satellite imagery  https://mapscaping.com/podcast/fake-satellite-imagery/   BTW  I have started a job board for geospatial people feel free to check it out!

A knowledge graph is a network of relationships between real work entities and in this episode, you will learn how and why knowledge graphs might be a better choice than spatial joins!    Further listening! The H3 Indexing System https://mapscaping.com/podcast/h3-geospatial-indexing-system/   Hex Tiles https://mapscaping.com/podcast/hex-tiles/   Points of Interest data https://mapscaping.com/podcast/all-of-the-places-in-the-world/   Dark Data https://mapscaping.com/podcast/unstructured-data-is-dark-data/   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images.    You might think that this is exactly what we are doing in earth observation but there are a few important differences between computer vision and what some people refer to as GeoAI.   This week Jordi inglada is going to help you understand what those differences are and why it's not always possible to use Computer vision techniques in the field of Remote Sensing.   Listen out for these key points during the conversation! Why plausible or realistic data is not always a substitute for actual measurements, except when it is ;)  In computer vision we can learn from the data, in earth observation we know the physics To do interesting work in data science you need to - Computer science, applied math, and domain expertise. You don’t need to be an expert in all three but you need to be interested in all three Vectors in the machine learning world don’t necessarily have anything to do with points lines and polygons ;)   Sponsored by Sinergise, as part of Copernicus Data Space Ecosystem knowledge sharing. dataspace.copernicus.eu/ http://dataspace.copernicus.eu/   Related Podcast Episodes   Super Resolution https://mapscaping.com/podcast/super-resolution-smarter-upsampling/ Fake Satellite Imagery https://mapscaping.com/podcast/fake-satellite-imagery/   Sentinal Hub https://mapscaping.com/podcast/sentinel-hub/ Google Earth Engine  https://mapscaping.com/podcast/introducing-google-earth-engine/   Microsofts Planetary Computer  https://mapscaping.com/podcast/the-planetary-computer/   BTW MapScaping has started a Job Board!  it's in the early stages but it's live Jobs - Mapscaping.com   Some more episodes you might enjoy   ESRI, GIS careers, Geospatial Data Science  QGIS, Geospatial Python, ArcGIS Pro Google Maps, Geomatics, Cartography Location Intelligence, Mapping

Data is what data does  - more about that later on ;)  This episode focuses on designing for privacy, how do we create value from location data without sacrificing personal privacy?  Well, you might start by adhering to the Enhanced Standards For Precise Location Information which means that information about sensitive places like churches, hospitals, military bases, and LGBTQ+ spaces isn't misused or sold. Plus, they protect our exact location from being shared with law enforcement or bounty hunters!  Yes, that's right bounty hunters!  You might also think about adding noise to the data, maybe you want to blur the time stamp and look at everything in aggregate? It turns out the location data is not just classified as personal data but is actually classified as sensitive personal data in privacy law so if you are going to work with it you really need to understand the risks involved. One of the really interesting ideas mentioned by Elizabeth Hein VP of Compliance & Data Protection was the idea of regulating Use, Harm, and Risk instead of sensitive data  Data Is What Data Does: Regulating Use, Harm, and Risk Instead of Sensitive Data If you want to learn more about POI data and why points of interest data are so hard  check out the episode called All of the Places in the World: https://mapscaping.com/podcast/all-of-the-places-in-the-world/ On a side note, I am working on a side project, it's a job board for geospatial people, and you can find it here https://mapscaping.com/jobs/ it's still in the development phase but feel free to check it out!

When comparing multispectral and hyperspectral data it is not simply a case of “more data more better”!  With hyperspectral you have “The curse of Dimensionality” but you also get more flexibility to pick exactly what bands you want to use! With multispectral you have less noise but you also have less data! This episode is designed to be a beginner's guide to the differences between hyperspectral and multispectral satellite data.   You can reach out to Gordon Logie here: https://sparkgeo.com/blog/team/gordon/   Podcast episode with the CEO of Sentinel-Hub https://mapscaping.com/podcast/sentinel-hub/   Here are some courses that focused on hyperspectral and offer further training   https://eo-college.org/courses/beyond-the-visible/ https://eo-college.org/courses/beyond-the-visible-imaging-spectroscopy-for-agricultural-applications/ https://www.enmap.org/events_education/hyperedu/

This week we are going to learn how Foursquare is trying to identify and map all of the places in the world!  Foursquare uses a mixture of crowd source and data conflation to maintain a database of 205 million places ... and it's not easy! Each phone might see the world slightly differently in terms of location accuracies and crowdsourcing data means that people "check-in" at different locations.    Kyle Fowler - Senior Director, Engineering at Foursquare Is going to give a behind-the-scenes look at how the "Orginal location-based social network" is trying to map all of the places in the world.  This episode is the first in a series of episodes I am going to publish in partnership with Foursquare and the idea is to use it as a reference for later episodes about Privacy and location data, Knowledge Graphs, AI, Location Based Marketing and Big geospatial Data in the Browser.

Protomaps is a serverless system for planet-scale maps, it's an umbrella project consisting of a few different components one of which is PMtiles. PMtiles is “Cloud Optimise Geotiff” for web mapping, what this means is that you can build a base map and host it without the need for a server!  PMtiles is a single file that you can access via HTTP range requests in the same way that you can access data within a Cloud Optimised Geotiff with the important difference that PMtiles can also contain vector data! What this means is that you can create your own base map, and host it on something like Amazon S3 object storage at a fraction of the cost of other base map solutions!    During this episode, you will hear Brandon, the founder, and creator of Protomaps, talk about scarcity, and well I have never really thought about base maps as being a scarce resource I can definitely see how a product like PMtiles could remove some of the barriers to entry for a lot of creativity in terms of base maps.    More information on Protomaps is here:  https://protomaps.com/   Tippecanoe https://github.com/felt/tippecanoe.git https://bertt.wordpress.com/2023/01/06/creating-vector-pmtiles-with-tippecanoe/ Relevant podcast episodes  Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/ Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/ Microsoft’s Planetary computer https://mapscaping.com/podcast/the-planetary-computer/ Stamen Design - Full Stack Cartography https://mapscaping.com/podcast/full-stack-cartography/   If you have any questions or comments, let me know, I would love to hear from you!

Storytelling with point clouds   This is not your typical point clouds episode! Today we are talking about how to use point clouds to tell a story. During this episode, you will hear Benjamin Muller talk about using a point cloud to make a film about the city of St Gallen in Switzerland and you might be tempted to think … what a waste of time! Why not use the data to make better measurements that lead to better decisions?  How many IT projects have failed, not because they were based on bad decisions but because they failed to get people to adopt the changes?  The best decisions are meaningless unless they are adopted.  So, how do we get people to change or adopt the change we are trying to make?  I think the first thing to understand is that packaging matters!  This episode is a case study into wrapping our ideas in a story and visualizing them using geospatial data.   Here is a link to the visualizations that were created using the point cloud data https://www.gruenesgallustal.ch/resume   You can take a look HxDR platform here https://hxdr.com/   If you are interested in more technical episodes about point clouds you might enjoy these!   The Point Data Abstraction Library https://mapscaping.com/podcast/pdal-point-data-abstraction-library/   Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/   Bathymetric Lidar https://mapscaping.com/podcast/bathymetric-lidar-and-blue-carbon/   Lidar from Drones https://mapscaping.com/podcast/lidar-from-drones/   Lidar from Space https://mapscaping.com/podcast/gedi-space-lasers/

You are about to meet Peter Spencer, a Freelance Archaeologist, Surveyor, and Geomatics Specialist You are also about to learn how geospatial tech and techniques are being applied in the field of archaeology at an object scale with laser scanning that enables fragments of skeletons from all over the world to be 3D printed and pieced together locally. At a site scale LiDAR, ground penetrating radar, and photogrammetry have completely changed the game in terms of modeling archaeological sites in a non-destructive manner. And at the landscape scale, Earth observation data and AI are being used to prospect for new sites!   You can connect with Pete here https://www.linkedin.com/in/peter-spencer-mcifa-162292136/   If you find this episode interesting you might like these episodes ;)  Using Synthetic Aperture Radar ( from space ) to find where drinking water is leaking out of pipes https://mapscaping.com/podcast/finding-water-leaks-from-space/    This episode is all about using LiDAR from drones https://mapscaping.com/podcast/lidar-from-drones/   The earth archive project is one archaeologist's goal to scan the entire world using LiDAR to create an archive for the future.  https://mapscaping.com/podcast/the-earth-archive/

Warning! this podcast episode is not as boring as it sounds!   While geospatial standards are boring on purpose ... this episode is not .- If you woke up this morning wanting to listen to a boring podcast episode about geospatial standards this is not for you!    Scott Simmons ( OGC’s Chief Standards Officer )  https://www.ogc.org/about/team/scott-simmons/ helps us understand what a Geopose is and how it might be used, why we need GeoRSS, and something called SensorThings!     You will also learn about the PubSub standard for the syndication of spatial data and why streaming data is not always the answer.   We also discover what the most boring geospatial standard is and when the shapefile "might" die.    All in all, this is a light-hearted look at a very serious topic and I hope you enjoy it!    What to know more about the Open Geospatial Consortium? this is a good place to start! https://mapscaping.com/podcast/the-open-geospatial-consortium/

It sounds like a clickbait title, right? And to be fair I am trying to capture your attention but this is not clickbait in the sense that the title makes a promise that the episode lives up to!  This is not a “get-rich-quick-scheme” its a story about someone like us who is earning money by using his geospatial skills to teach others. Konrad Hafen is a hydrologist with the USGS and runs two websites and a youtube channel, he is a geospatial content creator who makes money from ads and from selling online courses.     Websites https://geospatialschool.com/ https://opensourceoptions.com/ Youtube channel https://www.youtube.com/@opensourceoptions Each episode I publish has some sort of editorial intent, the intent of this episode is to show you a real-life example of someone like us who is using their geospatial knowledge if a different way.  Not because I think this is for everyone! But because I think the more examples we are exposed to of people doing something different the more options we might see in our own lives.  We don’t have to take advantage of these opportunities but there is some comfort in knowing that they are there. That we are not stuck.   If you listen to this episode and what to learn more about making money with geospatial content, Konrad and I will host a free webinar ( if we get enough interest! )  where we teach you the basics. Sign up here https://geospatialschool.com/webinar/

Distributing Geospatial Data - Every wondered why you might what to do this? Or maybe you understand the why but are unsure about the how?  Perhaps you have heard people talk about partitioning data or sharding data, you might have heard some of these terms used in the context of enterprise-scale geospatial systems and parallel processing and thought … Wow … that sounds daunting! This podcast episode is meant to be a soft introduction to the world of distributed geospatial databases and some of the concepts surrounding them.   Along the way, you will be introduced to something called Apache Sedona which is a cluster computing system for processing large-scale spatial data, and hear the surprising stat that 97% of enterprise data remains unused! … perhaps because of the lack of context?   You can connect with Mo Sarwat on  Twitter https://twitter.com/MoSarwat Or at https://mosarwat.org/ During the conversation, we mention PostgreSQL and PostGIS a few times which are topics that we have covered in previous podcast episodes   Servicing Dynamic Vector Tiles from PostGIS https://mapscaping.com/podcast/dynamic-vector-tiles-straight-from-the-database/   An introduction to PostgreSQL https://mapscaping.com/podcast/postgresql-an-open-source-geospatial-database-for-gis-practitioners/   Spatial SQL https://mapscaping.com/podcast/spatial-sql-gis-without-the-gis/ Toward the end of the conversation, we touch on the idea of cloud-native geospatial formats, and if you are interested in understanding this concept you might find these two previous episodes helpful   Cloud Optimized Point Clouds https://mapscaping.com/podcast/cloud-optimized-point-clouds/   Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/   If you have any questions or comments please feel free to reach out! I would love to hear from you

So you might be wondering why the United Nations World Food Programme needs a geospatial support unit. Let me give you a brief overview,    Basically, they curate and maintain global datasets that they use to model the risk of sudden-onset disasters than might lead to a food security risk. They use this model to send out early warnings to at-risk communities and help with the response when disasters happen.    Of course, there is more to it ...   But I will let Rohini Sampoornam Swaminathan ( The head of the Geospatial Support Unit at the UN World Food Programme)  explain it in more detail.   The Hunger Map https://hungermap.wfp.org/   BeforeAfterMap OSM Before-After Maps is an online tool that allows anyone to easily compare how a particular area looked in terms of OpenStreetMap (OSM) data at two different years, side by side, and get a visual insight into mapping contributions over time. https://beforeafter.baato.io/

Personally, I don't feel like aerial imagery gets the attention it deserves! So I invited Michael Bewley - Senior Director of AI Systems at Nearmap back on the podcast to help bring us up to speed on the state of the art of capturing, processing, and building a business around aerial imagery. If you don’t care about aerial imagery, think of this as a story about turning unstructured data into structured data into insights and building a business around that.   You can connect with Micheal on Twitter and LinkedIn https://twitter.com/michaelbewley https://www.linkedin.com/in/michaelbewley/   Listen out for the following highlights It's not a camera it's an imaging system! Detecting change is not hard, detecting meaningful change is hard Are human abilities still a good benchmark for AI systems?  How to determine if an AI system is a prototype or production ready   Previous Interview with Michael Bewley https://mapscaping.com/podcast/collecting-and-processing-aerial-imagery-at-scale/   Stratospheric Balloons As Remote Sensing Platforms https://mapscaping.com/podcast/cube-satellites-of-the-stratosphere/

This episode covers a wide range of topics from the role of geospatial in systems thinking - representing natural systems in location systems and how we can apply the technology behind virtual worlds to the real world. During this conversation, it became clear to me that technology should be used in service of culture and not the other way around. I think in terms of geospatial we have an interesting opportunity to have an outsized impact if we can just figure out how to market it!    I often think about problems as being either technological or cultural and it seems to me that technology generally moves faster than culture. Although I am sure we can all think of some great examples of huge cultural shifts that have happened in response to short-term emergencies in the past few years.    Culture seems to react to technology as opposed to driving technology. I think if we were all better at marketing we could change this.    Connect with Ingo Simonis https://www.linkedin.com/in/ingosimonis/   Previous Episodes featuring the Open Geospatial Consortium   The Open Geospatial Consortium https://mapscaping.com/podcast/the-open-geospatial-consortium/   Open Geospatial standards - shared standards to solve shared problems https://mapscaping.com/podcast/open-geospatial-standards-shared-standards-to-solve-shared-problems/

My guest on the show today is Mike. Today Mike is the founder of a software startup called ChroneChards, but he started as a cartographer for an adventure race and Patagonia. So he has come a long way. A couple of other really interesting points about Mike. He is a self-taught software developer and he is a self-taught businessman. The reason why I mentioned this means that if he can teach himself these things, then you can do it too. And as you'll discover later on in the episode, all you have to do is to get really, really comfortable with uncertainty.   You can connect with Mike here https://chrono.cards/ or on Twitter https://twitter.com/get_ChronoCards   It's probably worth riffing on this idea of uncertainty just for a minute.    I think this is one thing that we all have in common. There'll be something about our lives, professional or private that we are uncertain about.  I've had the pleasure over the last three, four, or five years of talking with a few different people that have overcome a lot of uncertainty.  People that are self-taught software developers like Mike, people that have started their own GIS geospatial consultancies, and build their own businesses around their skills, none of those people have overcome uncertainty. None of those people are now immune to uncertainty! They have simply learned to live with it.  So as someone who has experienced and continues to experience a lot of uncertainty, these stories have really helped me they've really had a positive impact. It's been great to know that other people go through this too.  So here are some links to those episodes and hopefully, they will inspire you as well.  From GIS Analyst to Software Engineer https://mapscaping.com/podcast/from-gis-analyst-to-software-engineer/ Starting your own geospatial consultancy  https://mapscaping.com/podcast/starting-your-own-geospatial-consultancy/ Self-employment in the GIS / Geospatial industry  https://mapscaping.com/podcast/self-employment-in-the-gis-geospatial-industry/ Being self-employed in the earth observation sector https://mapscaping.com/podcast/being-self-employed-in-the-earth-observation-sector/ Mid-career change https://mapscaping.com/podcast/mid-career-change/ I quit my job https://mapscaping.com/podcast/i-quit-my-job/

If 80% of all data has a spatial component why do we need to approach mature and emerging markets differently?  With everyone racing to democratize location technology why is GIS still a valuable skill?  What are consultancies looking for when they hire geospatial consultants?   Answers to these questions and much more in this episode where we focus on geospatial consulting, as a business and career path.    Todd Slind - VP of Technology at Locana.co Connect with Todd on Twitter or LinkedIn    Recommended Podcast Episodes  The Business of Web Maps Building a web-based mapping tool into a business A Business Built on Open-Source GIS Being A Graduate Geospatial Consultant Hiring and Being Hired for Geospatial Jobs Getting Where You Want To Go In Your Geospatial Career

Tropical forests are large sources of Greenhouse Gas (GHG) emissions, due to deforestation. They could instead be a big part of the climate solution.  Through Norway’s International Climate & Forests Initiative (NICFI), users can now access high-resolution, analysis-ready mosaics of the world’s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development for noncommercial uses.   MapComplete https://mapcomplete.osm.be   NICFI - Through Norway’s International Climate & Forests Initiative https://www.nicfi.no/   Planet NICFI https://www.planet.com/nicfi/

Cloud Optimized Point Clouds ( COPC) allow you to stream point cloud data!  Cloud Optimized Geotiffs made raster data streamable and now its time for Point Clouds to be just as accessible!    https://weeklyosm.eu/  weeklyOSM updates the community, on a weekly basis, about what is going on in the OSM Universe.   Thanks, OpenCage!  https://opencagedata.com/   Related Podcast Episodes   PDAL - Point Data Abstraction Library https://mapscaping.com/podcast/pdal-point-data-abstraction-library/   Cloud Native Geospatial https://mapscaping.com/podcast/cloud-native-geospatial/   Microsoft's Planetry Computer https://mapscaping.com/podcast/the-planetary-computer/     Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

We cover a lot of ground in this episode but some of the key takes are:   A key difference between visualizing non-spatial data and spatial data is that non-spatial data gives you the freedom to choose the locations of different objects.  Design is not a process, a way of thinking, and the outcome of not necessarily something visual We are getting closer to the promise of custom maps for everyone, and we are already seeing personalized maps    Content with Alan Mcconchie here: https://stamen.com/people/alan-mcconchie/ And learn more about Stamen Design here: https://stamen.com/   https://healthsites.io/ Building an open data commons of health facility data with OpenStreetMap   Recommended Episode about Cartography https://mapscaping.com/podcast/communicating-with-maps-the-art-of-cartography/     Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

Grow your career — or reimagine it entirely, what do you do if find yourself wanting to make a change halfway through your career? This episode with Thierry Gregorius from https://truehorizoncoaching.com/ is more of a discussion than an interview and if you are thinking about making a change in your career it might help you answer the question "what do I do next"    Recommended Podcast episodes focusing on careers in Geospatial    Getting Where you want to go in your geospatial career https://mapscaping.com/podcast/getting-where-you-want-to-go-in-your-geospatial-career/   Geospatial Side Hustles https://mapscaping.com/podcast/geospatial-side-hustles/   I quit my job https://mapscaping.com/podcast/i-quit-my-job/   Mentorship, leadership and career advice https://mapscaping.com/podcast/mentorship-leadership-and-career-advice/   Business ideas for geospatial people https://mapscaping.com/podcast/business-ideas-for-geospatial-people/   What is your idea https://mapscaping.com/podcast/what-is-your-idea/   Python Maps  https://mapscaping.com/podcast/python-maps/   For more episodes just visit https://mapscaping.com/podcasts/ and sort all the episodes by "geospatial career"   Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

This is a story about a peer-to-peer mapping technology that is enabling people to "fight maps with maps"   https://www.digital-democracy.org/ You can find Mapeo here https://www.digital-democracy.org/mapeo/   Promoting OSM projects OpenCage and Mapscaping are working together to help projects based on OpenStreetMap reach a wider audience. Projects will be selected to be featured on upcoming Mapscaping podcast episodes, with all costs covered by OpenCage! Apply here! https://forms.reform.app/NAvQ41/opencage-mapscaping/af9MFV   Recommended Podcast episodes about Off-line mapping MerginMaps - QGIS offline and in the feild https://mapscaping.com/podcast/qgis-offline-and-in-the-field/   https://mapscaping.com/podcast/two-mobile-data-collection-apps-you-need-to-know-about/   More podcast episodes on GIS and GIS careers can be found on our website https://mapscaping.com/podcasts/   Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

Applications of thermal imaging from space include monitoring wildfires, urban heat islands, economic activity, and the built environment.  But it's not easy ;)    Connect with Robin Cole at https://robmarkcole.com/   Check out the Earth Observation Hub! https://geoawesomeness.com/eo-hub/ More Geospatial Podcasts episodes    Recommended Podcast Earth Observation Podcasts   Fake Satellite Imagery  https://mapscaping.com/podcast/fake-satellite-imagery/   The LandSat Program https://mapscaping.com/podcast/the-landsat-program/   How To Keep Your Satellite Pointing At Earth https://mapscaping.com/podcast/the-landsat-program/   More podcast episodes on GIS and GIS careers can be found on our website https://mapscaping.com/podcasts/   Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

After 12 years of working as a GIS specialist / Consultant, I quit and I am ready to move on.    Looking back at the last 12 years if I could go back in time and give myself some advice I would tell myself these 8 things.   Boring Work won’t necessarily lead to interesting work Getting good at something typical leads to doing more of that thing Have more empathy for the professionals that you work with Think more about where you are trying to go Network Find a mentor Sometimes it's just not a good fit You don’t get to decide what is broken Things I need help with Share the podcast with a friend, a community anyone that you think might find it interesting or helpful I am looking for projects based on or around OpenStreetMap - this is part of a promotional campaign that I am doing together with OpenCage - More information here   More podcast episodes on GIS and Geospatial topics can be found on our website https://mapscaping.com/podcasts/   Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

It turns out that monitoring atmospheric pollution from space is really hard! But if you can do it will help you understand air quality, solar energy, ozone Layer and UV radiation, emissions and surfaces fluxes, and climate forcing    Contact Mark on Twitter https://twitter.com/m_parrington   Copernicus Atmospheric Monitoring Service https://atmosphere.copernicus.eu/ https://twitter.com/CopernicusECMWF Check out the Earth Observation Hub! https://geoawesomeness.com/eo-hub/ More Geospatial Podcasts episodes    Recommended Podcast Earth Observation Podcasts   Fake Satellite Imagery  https://mapscaping.com/podcast/fake-satellite-imagery/   The LandSat Program https://mapscaping.com/podcast/the-landsat-program/   How To Keep Your Satellite Pointing At Earth https://mapscaping.com/podcast/the-landsat-program/

The problem of unification: Spatial data comes in many different sizes, shapes, and formats making it a difficult and time-consuming process to join data for visualization, exploration, and analysis. Enter the Hex Tile system! Contact Foursquare:  connect.foursquare.com/mapscaping Check out Unfolded https://foursquare.com/products/unfolded/  https://www.unfolded.ai/  Introducing Hex Tiles: https://foursquare.com/article/introducing-hex-tiles-our-next-gen-tiling-system/   Example Maps https://studio.unfolded.ai/public/e86787a1-4871-4fbb-8eb8-33445e81ca73 https://studio.unfolded.ai/public/e3a6d2df-1784-4686-972b-9d3793c2515d https://studio.unfolded.ai/public/beb3cf0d-211d-4e5c-b08d-663da9a0631d     Recommended Podcast Episodes  Dynamic Vector Tiles Straight From The Database   H3 Geospatial Indexing System

This is a story about a hobby project called Scribble Maps that grew into business… but it's also a story about the opportunities and challenges of creating geospatial tools for non-geospatial professionals. Jonathan Wagner “Its easy to get people to make a map today, but how do you get them to come back and make a map tomorrow?” Special Offer for Listeners The MapScaping Audience ScibbleMaps.com/mapscaping   More podcast episodes on GIS and GIS careers can be found on our website https://mapscaping.com/podcasts/   Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/

This is a story about finding water on Mars and adapting the technology to look for leaking pipes underground here on earth ... but in this story, we don't just solve the problem, we build a business around it!   Lauren Guy From https://asterra.io/    Sponsored By https://geoawesomeness.com/eo-hub/    Recommended Podcast episodes  Introduction To Synthetic Aperture Radar ( SAR) Flood Monitoring From Space Counting Animals Using Satellite Imagery   More podcast episodes on GIS and GIS careers can be found on our website https://mapscaping.com/podcasts/   Consider supporting this podcast on Patreon  https://www.patreon.com/MapScaping?   Or go to MapScaping.com to find out about sponsoring our website reach out on Twitter https://twitter.com/MapScaping or LinkedIn https://www.linkedin.com/in/danielodonohue/