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

High-Precision Location for Everything That Moves

GPS gets you to the right street. Point One gets you to the right door, down to one or two centimeters. In this episode, I talk with Aaron, founder and CEO of Point One Navigation, about what it takes to deliver location 100 times more accurate than standard GPS, and why no single technology can do it alone.

We start in orbit and work our way down. Aaron explains how Point One uses every major satellite system (GPS, Galileo, GLONASS and BeiDou) and removes about 95 percent of the signal error, without any new satellites or chips. He also gives an honest take on the pros and cons of the new low Earth orbit constellations, and on why they won't magically give you centimeter accuracy indoors.

We then get into building an RTK correction network from scratch: thousands of one-by-one meter land leases, in-house hardware designed to run for 10 years in the middle of nowhere, and why "cables are the enemy." From there we talk about sensor fusion, and how combining RTK, low-cost motion sensors and physics-based models gets high performance out of cheap hardware.

We also cover the "multiplayer world," where robots, autonomous cars, forklifts and drone fleets share their location to coordinate safely. Then we look at sovereign infrastructure and the impact of GPS jamming, the hard problem of moving smoothly between indoors and outdoors, and how better maps and better location keep driving each other forward.

Aaron also talks about FUSE, a conference Point One is hosting on November 19th in San Francisco. If you're building anything that depends on location, this one is for you.

https://fuse.pointonenav.com/sf discount code: FUSEMAPSCAPING

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/

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 https://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.

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 means
Sandy'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 works
Every 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 signal
arrives 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 constellations above them. The
catch: a LEO pass is about 10 minutes horizon to horizon versuorbits are more disturbed by gravity variations and solaractivity, and if you want to broadcast inside the protected L-band there is a very large amount of spectrum paperwork ahead of you. Going to a higher band
gives smaller antennas and jamming resilience but brings back te TV owners know well.

Would we design GNSS differently today?
Sandy is careful here. There were good reasons for MEO, for L-band and for the signal structure. The one thing newer constellations like Galileo are
adding is authentication. GPS is a one-way broadcast with an o means unlimited passive users who never reveal themselves tothe system, and also an easy target for spoofing and jamming. Two-way systems like 5G can authenticate, but at the cost of a user limit and the user
having to identify themselves to the network.

Visual positioning versus GNSS
Daniel raises the LinkedIn claims that visual positioning is "killing GPS". Sandy's answer: visual positioning is how humans navigate, and it is excellent
at the immediate surroundings, which is exactly the dense urba But it has to be tied to a database of known coordinates tomean anything in an absolute sense, it is useless in the middle of the ocean, and it still needs a master clock, which almost always comes from GNSS. Her
framing: GNSS gives you a coordinate, visual positioning givesordinate does not mean you are not lost.

Terrestrial positioning
Wi-Fi is good enough to get you to the right city block or building, but decentralised access points with unverified coordinates make it neither precise
nor secure. 5G can do angle of arrival, which turns positionin problem instead of a resection, but the solution is computedat the network operator, not on your device, and only for members of the network. Ultra wideband and GNSS-like ground transmitters work well in warehouses
and mines but they are proprietary, someone has to install andsation always lags the first proprietary wave.

The future
Not one breakthrough technology, but seamless combination. A logistics vehicle that moves from the truck into the warehouse and back out again is still
hard to position across that boundary. Centimetre positioning rticularly small last-mile and warehouse robots that sharespace with people. Sandy also points out an inversion: reality capture treats moving objects as noise, while navigation treats them as the most important
thing in the scene.

Why positioning gets overlooked
Position is the given quantity in every physics problem, so nobody thinks about it until it is missing. Computer vision is intuitive because it emulates
what Sandy calls our "meat circuits and eyeballs". GNSS and innd estimation, but they are computationally light, need notraining data, and offer a capability humans do not have. And the thing that most surprises Sandy compared with ten years ago is that space is now a
legitimate topic. It is not just Star Trek anymore.

Connect with Sandy on LinkedIn
https://www.linkedin.com/in/sandy-kennedy-569a6a4/

Related episodes
SBAS, a base station in the sky
https://mapscaping.com/podcast/satellite-based-augmentation-system-a-base-station-in-the-sky/

Navigating the past, present and future of GNSS
https://mapscaping.com/podcast/navigating-the-past-present-and

Where does Google's blue dot come from?
https://mapscaping.com/podcast/how-google-calculates-your-location/

Alternate short description, if you prefer a question-led hook

Is visual positioning going to kill GPS? Why hasn't anyone just put GNSS satellites in low earth orbit? And why does every "GPS alternative" still need
GPS for its clock?

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

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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 https://www.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 https://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?