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

MapScaping
  • 4.7 (114)
  • Earth Sciences
  • Updated Weekly

A podcast for geospatial people. Weekly episodes that focus on the tech, trends, tools, and stories from the geospatial world. Interviews with the people that are shaping the future of GIS, geospatial as well as practitioners working in the geo industry. This is a podcast for the GIS and geospatial community subscribe or visit https://mapscaping.com to learn more

  1. 3d ago

    Smart Point Clouds: What If Every Point Knew What It Was

    Point clouds still feel a little bit like magic to me. You walk through a room, a city, or even just your living room with a scanner or your phone, and you can recreate it afterwards. But on their own, point clouds aren't actually that smart. So what would it mean for every single point to know not just where it is, but what it is, and how it relates to everything around it? My guest this week is Dr. Florent Poux, an adjunct professor, director of the 3D GeoData Academy, and author of 3D Data Science with Python. Florent has spent around 15 years on this one problem, and he developed the idea of the smart point cloud. And, as he pointed out just before we hit record, he's also a human, which we can't take for granted these days. In this conversation, we get into: What a smart point cloud is, and the layers behind it: geometry, semantics, topology, and behavior How you build one, whether you start with a survey scanner, a drone, or a phone video Why foundation models are great at proposing meaning but terrible at being accountable for it Why the boring, older machine learning approach is sometimes the right choice What changes when you can query your data in plain language, and why an agent should query a graph rather than read billions of points Why you should build your own tools rather than rent everyone else's Where world models, wearables, and 3D Gaussian splats fit into all of this And somewhere along the way, we come back to Borges and the old idea of a perfect one-to-one map of the world: beautiful, faithful, and completely useless. If you work with point clouds, 3D data, or AI in the geospatial world, this one is worth your time. Florent's 3D GeoData Academy and his book 3D Data Science with Python are great places to continue exploring. https://learngeodata.eu/

  2. Sep 23

    Cloud-Native Geospatial for Small Teams

    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

    Cloud-Native Geospatial for Small Teams

  3. Sep 17

    GERS, Metadata, and Maps Built for Machines

    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.

  4. Sep 7

    Sentinel Bird

    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.

  5. Aug 18

    Fields Of The World

    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

    Fields Of The World

  6. Aug 4

    Virtual Worlds for Physical AI Systems

    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

  7. Jul 29

    GPS, But 100 Times Stronger

    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.

  8. Jul 28

    Turning Aerial Imagery Into a Searchable World

    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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A podcast for geospatial people. Weekly episodes that focus on the tech, trends, tools, and stories from the geospatial world. Interviews with the people that are shaping the future of GIS, geospatial as well as practitioners working in the geo industry. This is a podcast for the GIS and geospatial community subscribe or visit https://mapscaping.com to learn more

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