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

Afleveringen

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  Virtual Worlds for Physical AI Systems 05.08.2026 49min

  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
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  GPS, But 100 Times Stronger 29.07.2026 43min

  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.
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  Turning Aerial Imagery Into a Searchable World 29.07.2026 55min

  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
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  Cloud Native for the Rest of Us 21.07.2026 38min

  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.
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  Mapping With 360 Cameras 15.07.2026 41min

  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?
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  Who Pays for Open Source? 02.07.2026 54min

  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.
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  The Great Retooling 24.06.2026 44min

  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
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  Earth Observation - The Invisible Industry 18.06.2026 1u 6min

  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?
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  10 Tools for Telling Stories With Maps 29.05.2026 1u 1min

  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  
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  Agents, Guardrails, and the Death of the Dashboard 14.05.2026 50min

  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.
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  How HOT Is Rethinking Drone Mapping 30.04.2026 50min

  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.  
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  Common Space 23.03.2026 38min

  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/ 
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  AI in QGIS 05.03.2026 49min

  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?
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  Geospatial Makers Start Building! 12.02.2026 46min

  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
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  Vibe Coding and the Fragmentation of Open Source 03.02.2026 36min

  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
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  A5 Pentagons Are the New Bestagons 19.01.2026 37min

  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
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  The Sustainable Path for Open Source Businesses 08.01.2026 36min

  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.
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  Free Software and Expensive Threats 26.12.2025 34min

  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.
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  Mapping Your Own World: Open Drones and Localized AI 18.12.2025 32min

  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
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  From Data Dump to Data Product 09.12.2025 45min

  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.