Explorez tous les épisodes du podcast 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.

 

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 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

Start building with an MCP-ready weather API today. The free developer tier gives you 15,000 free API calls every month. No credit card required. No expiry. Get your free weather API key at xweather.com.

 

Where does one field end and the next one begin? It sounds trivial right up until you try to answer it.

In this episode, I'm joined by Hannah Kerner — Assistant Professor at Arizona State University, AI Lead for NASA Harvest and NASA Acres, and Research Advisor for Taylor Geospatial — to talk about Fields of the World.

 

This episode is sponsored by the Cloud Native Geospatial Forum. The CNG Forum 2026 runs October 6–9 at Snowbird, Utah — three days of real-world cloud-native geospatial (STAC, COGs, GeoParquet, Zarr, and more) with the teams actually building this stuff at scale, plus a hands-on workshop day to kick things off. Register at https://2026.cloudnativegeo.org

 

 

In this episode I'm joined by Apurva Shah, co-founder and CEO of Duality AI, a company building virtual worlds — or "world models" for robots and physical AI systems.

Apurva's path here is an unusual one. He spent most of his career in animation, first at Pacific Data Images (which later became DreamWorks) and then over a decade at Pixar. His co-founder, Mike Taylor, comes from the other end of the spectrum entirely: a controls engineer who led field robotics at Caterpillar, deploying house-sized haul trucks at Australian mines. As Apurva puts it, if he's the pixels, Mike is the atoms.

We talk about why real-world data, as valuable as it is, is never enough on its own — and how synthetic data can be used to deliberately fill the gaps and biases that creep into any collected dataset.

Some of the things we get into:

• The difference between digital twins and 3D assets and why Duality treats twins as modular building blocks you compose into scenarios, rather than as one monolithic environment
• How they build environments from the ground up using DEM data, satellite imagery, photogrammetry and biome catalogues and why building them this way means everything is annotated from the start
• Calibrating virtual sensors against real ones, including synthetic aperture radar, and why sensor noise characteristics matter as much as physics
• Predicting how a material will behave across the spectrum (infrared, SAR) just from its visual response — and when that prediction breaks down
• Why "clutter" only becomes clutter once you know what you're looking for, and why it doesn't need to be perfect
• Modelling star fields for localisation in space, where there are no roads or buildings to navigate by
• Explicit versus generative world models, and why you need both
• A project with AWS simulating emergency ambulance routing through a city, complete with autonomous vehicles, traffic control and teleoperated human agents
• Where Duality is not the right tool molecular scale, virtual patients, drug discovery
• And yes, a story about robotics companies renting Airbnbs, trashing them, and leaving

Towards the end we get into the bigger questions: whether AI takes our jobs or makes us better at them, where the line sits between "good enough" and slop, and why Apurva — a self-described humanist — thinks virtual environments are the one place where human and machine intelligence can genuinely learn from each other.

Find out more at duality.ai, or dig into their technical writing at duality.ai/blogs

My guest today is Tyler Reid, co-founder and CTO of Xona, a company building the first commercial satellite navigation system.

We get into why Tyler and his team are moving satellites into low Earth orbit and what that unlocks. Stronger signals that can penetrate indoors, more resilient timing infrastructure, and better security against jamming and spoofing.

GPS sits 20,000 kilometres out. Xona sits at 1,100, with signals around 100 times stronger and a planned constellation of 258 satellites. Tyler came at this from the autonomous vehicle world at Ford, where the problem was simple enough: ten meters gets you to the store, but it doesn't keep a car in its lane.

We also talk about time, and how much of the world quietly depends on GPS to keep its clocks honest. Why countries are suddenly so interested in owning their own infrastructure. And the question Xona gets asked constantly: if you're broadcasting that close to GPS, aren't you the jamming problem?

If you're interested in what the future of GNSS might look like, you're really going to enjoy this one.

More at xonaspace.com, or reach out to Tyler on LinkedIn.

Vexcel isn't a household name — but you've almost certainly used their data. This aerial imaging company flies low-elevation aircraft across roughly 45 countries, capturing imagery at 7.5cm resolution from five different angles (straight down plus four oblique views), building one of the richest geospatial datasets on Earth.

In this episode, Daniel talks with Steve Lombardi, VP of Product at Vexcel, about what happens after the pixels are captured. They dig into object detection and "elements" (pre-extracted features like roof condition, solar panels, and pools), then go deep on Vexcel's newest capability: vector embeddings — essentially a searchable fingerprint for every 100-meter chunk of the planet.

Steve explains how customers can search the visible world with a text phrase, an uploaded image, or by simply drawing a box on the map — and get back matching locations anywhere on Earth. They cover how oblique imagery adds context to searches (like finding buildings that "look like a palace"), how customers refine results with a simple thumbs up/down feedback loop, and a fascinating new use case: exposing embeddings as a QGIS tile layer so you can build a personalized, concept-driven heat map — like a custom risk map — without ever touching a database.

Topics covered:

• What makes Vexcel's aerial imagery different from satellite imagery
• How photogrammetric data enables precise 3D measurement and object detection
• Object detection vs. vector embeddings — when to use which
• Custom elements: letting customers define their own objects to detect
• Searching aerial imagery by text, image, or drawn area
• Refining search results with a lightweight classifier ("thumb up / thumb down")
• Change detection over time (the Austin, Texas example)
• Bringing embeddings into QGIS as a personalized, concept-based tile layer
• Where aerial imagery and geospatial AI are headed next

What happens when you take cloud native geospatial out of the satellite-and-petabyte world and drop it into a small city with no budget and the world's slowest internet connection?

In this episode, Daniel talks with Nissim Lebovits, a city planner turned geospatial data scientist, about his nine months in Argentina building climate risk tools for under-resourced municipalities. Nissim breaks down what "cloud native" actually means in practice (hint: it's really about ease of access), why range requests let a small city grab 100MB instead of downloading a 20GB file, and how a single spatial join — run in about three seconds — revealed that 3 million people are missing from Argentina's own census data.

We also get into open building footprints, why QGIS (not Python) is where the real adoption is happening, the adoption gap holding cloud native geo back from small cities in the Global South, vibe-coding a QGIS plugin to finally make Argentina's census data usable, and where cloud native geo is headed over the next five years.

This episode is brought to you by the Cloud Native Geospatial Forum. CNG Forum 2026 runs October 6-9 at Snowbird, Utah — three days of real-world cloud native geospatial (STAC, COGs, GeoParquet, Zarr, and more) plus a hands-on workshop day on the 6th. Register at 2026.cloudnativegeo.org.

Jeffrey Martin is the co-founder and CEO of Mosaic, a company building 360-degree camera systems designed specifically for mapping. He's been obsessed with 360 imagery for over 20 years — he built one of the first websites combining panoramic images with a map back in 2005, before Google Street View existed, and he holds a Guinness World Record for a 320-gigapixel image of London stitched together from 52,000 photos.

In this episode, we get into what a modern mapping-grade 360 camera actually looks like, and why the difference between rolling shutter and global shutter sensors matters if you care about things like colorizing point clouds or photogrammetry. We also cover the surprisingly long tail of people who need up-to-date street-level imagery — everyone from departments of transport and utilities companies to playground designers and outdoor furniture salespeople.

A few things that stood out:

• Ground-level 360 capture fills gaps that drones and satellites simply can't — occlusion, permissions, and viewing angle all favor eye-level imagery for certain infrastructure work.
• Companies are taking very different bets on data collection strategy — Mosaic focuses on high-quality, purpose-built capture, while others like Hive Mapper are betting on scale and crowdsourced coverage instead.
• The camera hardware race may be plateauing, but the real frontier now is what we do with the imagery once it's collected — especially as large language models start being layered on top of geospatial data.

Where does this all go next? If AI can eventually parse and reason over an entire country's worth of street-level imagery, what does that unlock — and who ends up owning that layer of the map?

Open source software runs a huge chunk of the geospatial world — but somebody still has to pay for it.

In this episode I sit down with Marco Bernasocchi creator of QField and CEO of OpenGIS.ch, to dig into the awkward question most open source projects avoid: how do you keep something free and open while paying real people to build and maintain it?

Marco has been in the open source world since 2007, and he's grown QField into a tool with over two million downloads and a team of 14 behind it. We talk through how the money actually works — from sponsored feature development, to donations, to the cloud service that now funds most of what they do. Marco makes a compelling case that the real product isn't the software at all; it's convenience. You can always run it yourself. Paying just makes life easier — and keeps the project alive for everyone who can't.

We also get into why he refuses to say "free software," what maintainer burnout really looks like, and his advice for any developer quietly drowning in a project they love but can't afford to keep running.

A candid conversation about money, sustainability, and being a good citizen in the open source ecosystem.

Ian Schuler is the CEO of Development Seed — the team behind a lot of the open source tooling that quietly holds up the geospatial world. He's been at the helm for over a decade, and in this conversation, we dig into what he calls the great retooling: the idea that cloud-native geospatial is about to flip from an emerging pattern to the dominant one, and that AI is the thing tipping it over the edge.

The argument is simple — agents want to discover your data, query it, transform it, and hand back an answer. If your data isn't in a format they can reach, you're simply not part of the conversation anymore.

A really enjoyable one. I hope you get as much out of it as I did.

Register for the forum 👉 https://2026.cloudnativegeo.org

—

This episode is sponsored by the Cloud Native Geospatial Forum. The CNG Forum 2026 runs October 6–9 at Snowbird, Utah — three days of real-world cloud-native geospatial (STAC, COGs, GeoParquet, Zarr, and more) with the teams actually building this stuff at scale, plus a hands-on workshop day to kick things off. Register at https://2026.cloudnativegeo.org

What is Earth observation, really — and why, after fifty years of satellite imagery, is it still not "mainstream"?

In this episode, I'm joined by Aravind Ravichandran, founder of TerraWatch, an independent research and advisory firm focused entirely on Earth observation. Aravind writes the TerraWatch newsletter, runs the EO Summit, and spends his time thinking about the strategy and economics of the industry more deeply than just about anyone.

We start with a deceptively simple question — is Earth observation even an industry? — and end up somewhere more interesting: Aravind's argument that when the technology truly succeeds, it becomes invisible, quietly embedded in agriculture, insurance, energy, and defense the same way weather satellites already are.

Along the way, we get into:

• Why 60+ countries are now building their own satellite constellations, and whether they'll still exist in five years
• What Planet restricting imagery access really means — and why Aravind thinks they were "punished for doing something progressive"
• The technology is actually moving the needle: hyperspectral data going free, AI foundation models, edge computing on satellites, and inter-satellite laser links
• Which use cases are genuinely picking up (utilities, parametric insurance) — and which were always hype (counting cars in parking lots)
• The defense paradox: how the industry that built Earth observation may also be the biggest thing holding back its commercial future

Some open questions we sit with: If satellite data is critical infrastructure, what happens when someone turns it off?

Should high-resolution imagery of the whole world be open — and what are the privacy and security costs if it is? And can sixty countries ever pool their data, or will sovereignty always trump logic?

Ryan Shields has one of the most interesting careers in geospatial — from remote sensing for conservation in the Caribbean, to disaster response data engineering with FEMA, to his current role turning spatial data into animation assets for Johnny Harris's YouTube channel at New Press.

In this episode, Ryan counts down the 10 tools he's using right now to tell map stories that reach millions of viewers. We cover Felt, PostGIS on Crunchy Bridge, Geo Layers 3 for After Effects, CShapes for historical borders, Natural Earth, MapTiler, Mapshaper, the new GDAL pipeline syntax, GRASS GIS, and how he's stitching it all together with Claude Code and VS Code.

Along the way we get into how LLMs are changing geospatial workflows, why command-line tools are well-suited to AI agents, the limits of de facto vs de jure borders in historical datasets, and how better tooling is making data journalism viable for small communities that newsrooms usually overlook.

Whether you're a cartographer, data engineer, journalist, or just map-curious, this one is packed with links worth chasing.

 

Tools & resources mentioned in this episode

• Felt — https://felt.com
• PostGIS — https://postgis.net
• Crunchy Bridge — https://www.crunchybridge.com
• Geo Layers 3 (After Effects extension) — https://aescripts.com/geolayers/ ⚠️ verify
• CShapes (historical borders dataset) — https://icr.ethz.ch/data/cshapes/ ⚠️ verify
• Open Historical Map — https://www.openhistoricalmap.org
• Natural Earth — https://www.naturalearthdata.com
• Eduard (Swiss-style hillshading app) — https://www.eduard.earth ⚠️ verify
• Shaded Relief (Tom Patterson) — https://www.shadedrelief.com
• MapTiler — https://www.maptiler.com
• MapTiler Engine — https://www.maptiler.com/engine/
• EPSG.io — https://epsg.io
• Mapshaper — https://mapshaper.org
• GDAL — https://gdal.org
• GRASS GIS — https://grass.osgeo.org
• QGIS — https://qgis.org
• DBeaver — https://dbeaver.io
• Claude Code — https://claude.com/claude-code ⚠️ verify
• VS Code — https://code.visualstudio.com
• Geodata Viewer (VS Code extension) — search "Geodata Viewer" in the VS Code marketplace
• PAI – Personal AI Infrastructure (Daniel Miessler) — https://github.com/danielmiessler ⚠️ verify exact repo
• Deep State Map (Ukraine conflict) — https://deepstatemap.live
• Johnny Harris (YouTube) — https://www.youtube.com/@johnnyharris

Projects I'm working on

• Quick Map Tools — https://quickmaptools.com
• Hunting NZ — https://huntingnz.com
• NZ Elevation Tools — https://nzelevationtools.com
• Smart Query Tools — https://smartquerytools.com

 

Nadine Alameh is back — former CEO of the Open Geospatial Consortium, and now CEO and co-founder of Lunate AI, a six-month-old company sitting right at the messy intersection of geospatial and AI.

In this conversation, Nadine breaks down the three types of clients she's seeing right now: government agencies standing at the edge of the river, wondering whether to jump in, startups from outside the geospatial world stumbling in with big ideas, and organizations that know they need to modernize but don't know who to call.

We get into why the real value today is in experience and advisory rather than raw coding, why "moving up the stack" matters more than ever, and how AI agents are quietly reshaping everything — from how satellites get tasked to how dashboards (or whatever replaces them) get built.

We also talk about the death of the one-size-fits-all dashboard, world models and simulations, why trust and guardrails are the actual hard work, and what it takes to go from a flashy proof-of-concept to something a bank can rely on every morning.

If you're a GIS professional thinking about where to position yourself, a startup founder wandering into the geospatial world, or someone trying to figure out how AI fits into your workflows — this one's for you.

What happens when you put professional-grade aerial mapping in the hands of the people who actually live in the places being mapped?

In this episode, I'm joined by Rebecca Firth, Executive Director of the Humanitarian OpenStreetMap Team (HOT) — a global community of around 750,000 people building free and open-source maps in the places that need them most.

We dig into HOT's Drone Tasking Manager: a tool that lets local residents, using low-cost consumer drones, capture professional-quality aerial imagery of their own communities. Rebecca explains how it works under the hood, how dozens of pilots can coordinate to produce a single seamless mosaic, and the assumptions her team got wrong along the way — from over-engineered task locking to worrying about the wrong problems entirely.

We also talk about what this looks like on the ground in Freetown, Sierra Leone, where the same drone imagery is now being used across seven city departments — for waste collection planning, disability access, flood mitigation, and soon, thermal mapping during heat waves to support local-led climate adaptation.

If you care about mapping, drones, open data, or the simple idea that local people with local tools can solve problems faster than anyone flying in from outside — this one's for you.

 

 Thank you to today's sponsor, Geo Business - Registration is free.

 

This episode examines the Common Space initiative, a non-profit project dedicated to building and launching high-resolution optical satellites designed specifically for humanitarian purposes, such as aiding populations at risk from climate events and conflict. 

Although there are over a thousand Earth observation satellites currently in orbit, high-resolution imagery remains largely inaccessible to humanitarians, journalists, and civil rights groups due to high costs, restrictive licensing, and the prioritization of defense and intelligence tasking. 

Common Space aims to bridge the gap between low-resolution public goods (like Landsat and Sentinel) and expensive commercial options by offering 50 to 70-centimeter resolution imagery with open licensing. 

The project plans to utilize a "club good" funding model, where humanitarian groups can access the data for free, while commercial and government entities pay to participate to fund the system's continued operations.

How will a community-driven governance model successfully navigate the ethical risks and potential misuse of releasing high-resolution conflict data in real-time?

 

Learn more about Commonspace here

https://www.commonspace.world/ 

 

Or connect with the founders here

https://www.linkedin.com/in/billfgreer/ 

https://www.linkedin.com/in/rhiannan-price/ 

I've been playing around with a lot of large language models lately, and it is absolutely fascinating to watch them work. But what happens when you bring that directly into QGIS?

Right now, AI in the geospatial industry is a lot like a fast, enthusiastic new intern, incredibly helpful, and sometimes completely wrong, but improving at a rate that no human can compete with. 

As we hand more of our geoprocessing tasks over to these algorithms, and computing becomes more pervasive, are our own GIS skills becoming obsolete? Or are we just unlocking radically different opportunities to rethink our careers?

Geospatial Product Swiss Army Knife 1. The "Build It and They Won't Come" Trap

We have all seen it: a talented geospatial professional spends months—perhaps years—perfecting a technically sophisticated web map or a niche data service, only to release it to a deafening silence. In our industry, the "build it and they will come" philosophy is a fast track to zero traction.

Precision is the enemy of progress when it is applied to the wrong problem.

Daniel and Stella Blake Kelly explored a remedy for this pattern. Stella—a New Zealand-born, Sydney-based strategist and founder of the consultancy Cartisan—didn’t start with a master plan. She "fell into" the industry after being inspired by a lecturer with bright blue hair and a passion for GIS that rivaled a Lego builder’s creativity. Today, she helps organizations move from "making things" to "building products that matter" using a framework she calls the Product Swiss Army Knife.

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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:

1. Vision: Establish a clear statement of what you are building and why it needs to exist.
2. User Needs: Move beyond assumptions to identify real users and their specific friction points.
3. Market & Context: Analyze the existing ecosystem (competitors, data, and workflows) to find your gap.
4. Features: Ruthlessly prioritize "must-haves" to define a lean Minimum Viable Product (MVP).
5. Prototypes & User Flows: Map out the user’s journey through the service before writing a line of code.
6. Proof of Concept: Create a tangible, working version to prove the technical and market logic.
7. 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."

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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.

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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 programmatic advertising revenue.

The reason? Market Context. The keyword "roofing" implies high-value intent; a user measuring their roof is likely in the market for a new one, making them incredibly valuable to advertisers. Understanding the commercial landscape surrounding a user's problem is the difference between a struggling hobby project and a viable MicroSaaS.

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5. The Precision Paradox: Why GIS Experts Struggle with UX

There is a fundamental tension between the geospatial technical mindset and the product design mindset. GIS professionals are trained to be exact, precise, and correct. Designers, however, are taught to be wrong, gather feedback, and iterate.

Daniel illustrated this with a "Hot Jar" anecdote. He once built a site where users were failing to move through the revenue funnel. Heat maps revealed the issue wasn't the data—it was the layout. Users weren't scrolling down far enough to see the critical action button. The data was perfect, but the UX was broken.

Stella emphasizes that building a product requires the humility to accept that "the best designers of products are the users themselves." Success often comes from moving a button or simplifying a flow, not from adding another decimal point of precision to the underlying geometry.

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6. Launching "Soft" to De-Risk the Rollout

The "perfectionism trap" is the primary reason geospatial products fail to launch. Builders fear that "releasing slop" will damage their brand. However, Stella suggests the Soft Launch (Tool #7) as a vital de-risking mechanism.

A soft launch allows you to:

• Prevent Stagnation: Avoid the "quiet abandonment" of projects that never see the light of day.
• Validate Demand: Ensure people actually want the tool before committing to months of development.
• Build Brand and Trust: In a world where anyone can spin up a tool with AI, trust is the ultimate differentiator.

Launching early ensures continuous improvement and prevents the high-stakes pressure of a single "grand opening" that may miss the mark entirely.

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7. Conclusion: The Final Ponderance

Building successful geospatial products is about empathy and process, not just pixels and polygons. Whether you are building a global API or an internal tool for a government agency, the principles of the Swiss Army Knife remain the same.

At the recent Phosphag workshop in Oakland, the range of products—from print maps to digital twins—all shared a common hurdle: the energy to push through the "perfection barrier."

As you look at your current projects, ask yourself: Am I building this because the data exists, or because a human has a problem I can solve?

Success in the modern landscape requires a diversity of skills—brand, marketing, and distribution. If you aren't embarrassed by your first version, you’ve already lost the market. Stop building in the dark. Get out there and build the thing.

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, documentation has become the primary way we "market" our code to the machines. If your open-source project lacks a clean README or a rigorous specification, it is effectively invisible to the AI-driven future of development. By investing in high-quality documentation, developers are engaging in a form of technical SEO. You are ensuring that when an AI looks for the "signal" in the noise, it chooses your vetted library because it is the most readable and reliable option available.   From Software Developers to Software Designers   The role of the geospatial professional is shifting from writing syntax to what Hansen calls the "Foundry" model. Using tools like GitHub Specit, the human acts as a designer, defining rigorous blueprints, constraints, and requirements in human language. The machine then executes the "how," while the human remains the sole arbiter of the "what" and "why." Hansen’s advice for the next generation—particularly those entering a job market currently hostile to junior engineers—is to abandon generalism. Don't just learn to code; become a specialist in a domain like geospatial. The ability to write Python is becoming a commodity, but the ability to design a system that accounts for the nuances of remote sensing is an increasingly rare and valuable asset.   History Repeats: The "Priesthood" of Assembly   This shift mirrors the 1950s, when the "priesthood" of assembly programmers looked at the first compilers with deep suspicion. Kathleen Booth, who wrote the first assembly language, lived in a world where manual coding was an arcane, elite skill. Those early programmers argued that compilers were untrustworthy and that a human could always write "better" code by hand. They were technically right about efficiency, but they were wrong about the future. Just as the compiler was "good enough" to allow us to move "up the stack" and take on more complex problems, AI is the next level of abstraction. We might use a "Ralph Wiggum script"—a loop that feeds AI output back into itself until the task is "done"—and while it may be a brute-force method, it is often more productive than the perfection of the past.   Conclusion: The Future is a Specialist's Game   We are moving away from being the writers of code and toward being the designers of systems. While the "syntax wall" has been demolished, the requirement for domain knowledge has only grown higher. The keyboard isn't dying; it is being repurposed for higher-level architectural thought.   As the industry experiences a "recursive improvement" of these tools, the question for every professional is no longer about whether the machine can do your job. It’s whether you have the specialized expertise to tell the machine what a "good enough" job actually looks like. Are you prepared to stop being a coder and start being a designer?

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 properly normalized.   Introducing A5: A New System Built for Accuracy A5 is a new DGGS designed from the ground up to prioritize analytical accuracy. It is based on a dodecahedron, a 12-sided shape with pentagonal faces that is, in the words of its creator, "even less spiky" than H3's icosahedron.   The motivation for A5 came from a moment of discovery. Its creator, Felix Palmer, stumbled upon a unique 2D tiling pattern made of irregular pentagons. This led to a key question: could this pattern be extended to cover the entire globe? The answer was yes, and it felt like uncovering something "very, very fundamental." This sense of intellectual curiosity, rather than a narrow business need, is the foundation upon which A5 is built. A5's single most important feature is that it is a true equal-area system. Using a specific mathematical projection, A5 ensures that every single cell at a given resolution level has the exact same area. This guarantee even accounts for the Earth's true shape as a slightly flattened ellipsoid, not a perfect sphere.   This is a game-changer for analysis. By providing cells of identical size, A5 eliminates the need for analysts to perform complex area-based normalization. This prevents a common source of error and dramatically simplifies workflows when calculating metrics like population density, risk exposure, or any other value that depends on a consistent spatial unit.   A5 vs. H3 vs. S2: A Head-to-Head Comparison The choice of base polyhedron and projection method results in significant differences between the major DGGS. Here is a direct comparison of their key technical characteristics. Metric A5 H3 S2 Base Polyhedron Dodecahedron (12 pentagonal faces) Icosahedron (20 triangular faces) Cube (6 square faces) Equal-Area Cells Yes (Exact) No (Up to 2x area variation) No Max Resolution ~30 square millimeters ~1 square meter ~1 square centimeter Global Hierarchy Yes (Single top-level world cell) No (122 top-level cells) Yes (6 top-level cells)   The A5 Ecosystem and its "Polyglot Mirroring" Approach   The success of H3 proves that a powerful mathematical system is not enough; it needs a rich ecosystem of accessible tools to gain adoption. A5 is being built with this principle in mind, but with a novel development strategy.   This approach is called "polyglot mirroring." Instead of building a single core library in C and creating language bindings, A5 maintains separate, complete, and equivalent codebases in multiple languages, including TypeScript, Python, and Rust. To keep these distinct codebases synchronized, Large Language Models (LLMs) are used to port changes and new features from one language to another. This strategy makes the system more accessible and maintainable for developers within each language's native community.   The power of this approach was proven in a true "wow moment" during A5's development. The creator, having never written a single line of Rust, fed the existing TypeScript and Python versions and a comprehensive test suite to an LLM. After about a week of guided iteration, the model produced a complete, working, high-performance Rust library. This demonstrates how modern tools can enable a single developer to build and maintain a truly multi-lingual ecosystem, something that would have been impossible just a few years ago.   Conclusion: When Should You Choose A5?   A5 offers a powerful and precise alternative to existing global grid systems. Its primary advantages make it the ideal choice for specific, demanding use cases.   • Statistical Validity: Any analysis where equal-area cells are paramount for accuracy is a prime candidate for A5. This includes density mapping, demographic studies, environmental modeling, and financial risk assessment. • Extreme Resolution: For applications requiring precision beyond what H3 or S2 can offer, A5's ability to index down to cells of approximately 30 square millimeters provides unmatched granularity. • Efficient Global Hierarchy: Workflows that need to query data at a global scale benefit from A5's simple hierarchy, which starts from a single cell representing the entire world. In contrast, loading global data with H3's 122 top-level cells could require 122 separate requests, creating unnecessary complexity and inefficiency. To explore the A5 system, see detailed visualizations, and understand the technical comparisons in more depth, visit the official website at a5geo.org.  

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

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

What if communities could map their own worlds using low-cost drones and open AI models instead of waiting for expensive satellite imagery?

In this episode with Leen from HOT (Humanitarian OpenStreetMap Team), we explore how they're putting open mapping tools directly into communities' hands—from $500 drones that fly in parallel to create high-resolution imagery across massive areas, to predictive models that speed up feature extraction without replacing human judgment.

Key topics:

• Why local knowledge beats perfect accuracy
• The drone tasking system: how multiple pilots map 80+ square kilometers simultaneously
• AI-assisted mapping with humans in the loop at every step
• Localizing AI models so they actually understand what buildings in Chad or Papua New Guinea look like
• The platform approach: plugging in models for trees, roads, rooftop material, waste detection, whatever communities need
• The tension between speed and OpenStreetMap's principles
• Why mapping is ultimately a power game—and who decides what's on the map

This conversation with Jed Sundwall, Executive Director of Radiant Earth, starts with a simple but crucial distinction: the difference between data and data products. And that distinction matters more than you might think.

We dig into why so many open data portals feel like someone just threw up a bunch of files and called it a day. Sure, the data's technically "open," but is it actually useful? Jed argues we need to be way more precise with our language and intentional about what we're building.

A data product has documentation, clear licensing, consistent formatting, customer support, and most importantly - it'll actually be there tomorrow.

From there, we explore Source Cooperative, which Jed describes as "object storage for people who should never log into a cloud console." It's designed to be invisible infrastructure - the kind you take for granted because it just works. We talk about cloud native concepts, why object storage matters, and what it really means to think like a product manager when publishing data.

The conversation also touches on sustainability - both the financial kind (how do you keep data products alive for 50 years?) and the cultural kind (why do we need organizations designed for the 21st century, not the 20th?). Jed introduces this idea of "gazelles" - smaller, lighter-weight institutions that can move together and actually get things done.

We wrap up talking about why shared understanding matters more than ever, and why making data easier to access and use might be one of the most important things we can do right now.

Reflections from the FOSS4G 2025 conference 

 

Processing, Analysis, and Infrastructure (FOSS4G is Critical Infrastructure)

The high volume of talks on extracting meaning from geospatial data—including Python workflows, data pipelines, and automation at scale—reinforced the idea that FOSS4G represents critical infrastructure.

• AI Dominance: AI took up a lot of space at the conference. I was particularly interested in practical, near-term impact talks like AI assisted coding and how AI large language models can enhance geospatial workflows in QGIS. Typically, AI discussions focus on big data and earth observation, but these topics touch a larger audience. I sometimes wonder if adding "AI" to a title is now like adding a health warning: "Caution, a machine did this".
• Python Still Rules (But Rust is Chatting): Python remains the pervasive, default geospatial language. However, there was chatter about Rust. One person suggested rewriting QGIS in Rust might make it easier to attract new developers.

Data Infrastructure, Formats, and Visualization

When geospatial people meet, data infrastructure—the "plumbing" of how data is stored, organized, and accessed—always dominates.

• Cloud Native Won: Cloud native architecture captured all the attention. When thinking about formats, we are moving away from files on disk toward objects in storage and streaming subsets of data.
• Key cloud-native formats covered included COGs (Cloud Optimized GeoTIFFs), Zarr, GeoParquet, and PMTiles. A key takeaway was the need to choose a format that best suits the use case, defined by who will read the file and what they will use the data for, rather than focusing solely on writing it.
• The Spatial Temporal Asset Catalog (STAC) "stole the show" as data infrastructure, and DuckDB was frequently mentioned.
• Visualization is moving beyond interactive maps and toward "interactive experiences". There were also several presentations on Discrete Global Grid Systems (DGGS).

Standards and Community Action

• Standards Matter: Standards are often "really boring," but they are incredibly important for interoperability and reaping the benefits of network effects. The focus was largely on OGC APIs replacing legacy APIs like WMS and WFS (making it hard not to mention PyGeoAPI).
• Community Empowerment: Many stories focused on community-led projects solving real-world problems. This represents a shift away from expert-driven projects toward community action supported by experts. Many used OSM (OpenStreetMap) as critical data infrastructure, highlighting the need for locals to fill in large empty chunks of the map.

High-Level Takeaways for the Future

If I had to offer quick guidance based on the conference, it would be:

1. Learn Python.
2. AI coding is constantly improving and worth thinking about.
3. Start thinking about maps as experiences.
4. Embrace the Cloud and understand cloud-native formats.
5. Standards matter.
6. AI is production-ready and will be an increasingly useful interface to analysis.

Reflections: What Was Missing?

The conference was brilliant, but a few areas felt underrepresented:

• Sustainable Funding Models: I missed a focus on how organizations can rethink their business models to maintain FOSS4G as critical infrastructure without maintainers feeling their time is an arbitrage opportunity.
• Niche Products: I would have liked more stories about side hustles and niche SAS products people were building, although I was glad to see the "Build the Thing" product workshop on the schedule.
• Natural Language Interface: Given the impact natural language is having on how we interact with maps and geo-data, I was surprised there wasn't more dedicated discussion around it. I believe it will be a dominant way we interact with the digital world.
• Art and Creativity: Beyond cartography and design talks, I was surprised how few talks focused on creative passion projects built purely for the joy of creation, not necessarily tied to making a part of something bigger.

Karl returns to the Mapscaping podcast to discuss his latest venture, Tyche Insights - a platform aimed at building a global community of geospatial storytellers working with open data.

In this conversation, we explore the evolution from his previous company, Building Footprint USA (acquired by Lightbox), to this new mission of democratizing public data storytelling.

Karl walks us through the challenges and opportunities of open data, the importance of unbiased storytelling, and how geospatial professionals can apply their skills to analyze and share insights about their own communities. Karl shares his vision for creating something akin to Wikipedia, but for civic data stories - complete with style guides, editorial processes, and community collaboration.

 

Featured Links

Tyche Insights:

• Main website: https://tycheinsights.com
• Wiki platform: https://wiki.tycheinsights.com
• Example project: https://albanydatastories.com

Mentioned in Episode:

• USAFacts: https://usafacts.org
• QField Partner Program: https://qfield.org/partner
• Open Data Watch: (monitoring global open data policies)

AI Slop: An Experiment in Discovery

Solo Episode Reflection: I'm back behind the mic after about a year-long break. Producing this podcast takes more time than you might imagine, and I was pretty burnt out. The last year brought some major life events, including moving my family back to New Zealand from Denmark, dealing with depression, burying my father, starting a new business with my wife, and having a teenage daughter in the house. These events took up a lot of space.

The Catalyst for Return: Eventually, you figure out how to deal with grief, stop mourning the way things were, and focus on the way things could be. When this space opened up in my life, AI came into the picture. AI got me excited about ideas again because for the first time, I could just build things myself without needing to pitch ideas or spend limited financial resources.

On "AI Slop": I understand why some content is called "slop," but for those of us who see AI as a tool, I don't think the term is helpful. We don't refer to our first clumsy experiments with other technologies—like our first map or first lines of code—as slop. I believe that if we want to encourage curiosity and experimentation, calling the results of people trying to discover what's possible "slop" isn't going to help.

 

My AI Experimentation Journey

My goal in sharing these experiments is to encourage you to go out and try AI yourself.

Phase 1: SEO and Content Generation My experimentation began with generating SEO-style articles as a marketing tool. As a dyslexic person, I previously paid freelancers thousands of dollars over the years to help create content for my website because it was too difficult or time-consuming for me to create myself.

• Early Challenges & Learning: My initial SEO content wasn't great, and Google recognized this, which is why those early experiments don't rank in organic search. However, this phase taught me about context windows, the importance of prompting (prompt engineering), and which models and tools to use for specific tasks.
• Automation and Agents: I played around with automation platforms like Zapier, make.com, and n8n. I built custom agents, starting with Claude projects and custom GPTs. I even experimented with voice agents using platforms like Vappy and 11 Labs.

Unexpected GIS Capabilities: During this process, I realized you can ask platforms like ChatGPT to perform GIS-related data conversions (e.g., geojson to KML or shapefile using geopandas), repro data, create buffers around geometries, and even upload a screenshot of a table from a PDF and convert it to a CSV file. While I wouldn't blindly trust an LLM for critical work, it's been interesting to learn where they make mistakes and what I can trust them for.

AI as a Sparring Partner: I now use AI regularly to create QGIS plugins and automations. Since I often work remotely as the only GIS person on certain projects, I use AI—specifically talking to ChatGPT via voice on my phone—as a sparring partner to bounce ideas off of and help me solve problems when I get stuck.

Multimodal Capabilities: The multimodal nature of Gemini is particularly interesting; if you share your screen while working in QGIS, Gemini can talk you through solving a problem (though you should consider privacy concerns).

 

The Shift to Single-Serve Map Applications

I noticed that the digital landscape was changing rapidly. LLMs were becoming "answer engines," replacing traditional search on Google, which introduced AI Overviews. Since these models no longer distribute traffic to websites like mine the way they used to, I needed a new strategy.

• The Problem with Informational Content: Informational content on the internet is going to be completely dominated by AI.
• The Opportunity: Real Data: AI is great at generating content, but if you need actual data—like contours for your specific plot of land in New Zealand—you need real data, not generated data.
• New Strategy: My new marketing strategy is to create targeted, single-serve map applications and embed them in my website. These applications do one thing and one thing only, using open and valuable data to solve very specific problems. This allows me to rank in organic search because these are problems that LLMs have not yet mastered.

Coding with AI: I started by using ChatGPT to code small client-side map applications, then moved to Claude, which is significantly better than OpenAI's models and is still my coding model of choice. Currently, I use Cursor AI as a development environment, swapping between Claude code, OpenAI's Codex, and other models.

• A Caveat: Using AI for coding can be incredibly frustrating. The quality of the code drops dramatically once it reaches a certain scale. However, even with flaws, it’s a thousand times better and faster than what I could do myself, making my ideas possible. Crucially, I believe that for the vast majority of use cases, mediocre code is good enough.

 

Success Story: GeoHound

After practicing and refining my methods, I decided to build a Chrome extension. Every GIS professional can relate to the pain point of sifting through HTTP calls in the developer tools networking tab to find the URL for a web service to use in QGIS or ArcGIS.

• The Impossible Idea Made Possible: I had pitched this idea to multiple developers in the past, who were either uninterested or quoted between $10,000 and $15,000 to build it.
• The AI Result: Using AI, I had a minimum viable Chrome extension—GeoHound—that filtered out common geo web services within 3 hours. It took a few days of intermittent work before it was published to the Chrome and Edge web stores.
• Current Use: GeoHound has thousands of users (my own statistics suggest closer to or over 3,000 users, compared to the 1,000 shown on the Chrome store). While not perfect, it is clearly good enough, and this was something that was impossible for me just six months ago.

 

My Point: Now is the Time to Experiment

AI is here, and it will lead to profound change. Experimenting with it is vital because it will:

1. Help you develop the skills and knowledge needed to meet the needs of the people you serve.
2. Help you better understand what is hype and what is not, allowing you to decipher which voices to listen to.

We are moving from a world where information is ubiquitous to a world where knowledge is ubiquitous. Now is the time to be making sloppy mistakes. Don't let perfection stop you from learning how to make stuff that is going to be good enough.

If your work consists of repetitive tasks that follow step-by-step recipes, that's going to be a tough gig going forward. Long-term, there will be new opportunities, but you need to be experimenting now to be in a position to take advantage of them.

Resources Mentioned

You will find a list of the tools I've been experimenting with in the show notes.

• Automation: make.com, n8n, Zapier
• Voice/Agents: 11 Labs, Vappy, custom GPT (MCP servers)
• Coding Models: Claude (current choice), OpenAI's Codex, ChatGPT
• Development Environment: Cursor AI
• LLMs/Multimodal: Gemini (studio.google.com)
• Browser Extension: GeoHound (for Chrome and Edge)

https://chromewebstore.google.com/detail/nooldeimgcodenhncjkjagbmppdinhfe?utm_source=item-share-cb

If you build anything interesting with these tools, please let me know! I'd love to hear about your own experiments.

 

Jonathan Wagner, CEO of Scribble Maps, is back on the podcast, and this time we're talking about Scribble—an AI agent he's built into his platform. Not a chatbot, an agent. There's a difference, and we get into that.

https://mapscaping.com/podcast/the-business-of-web-maps/ 

So far, Scribble has access to 140 tools. It can view your map, select tools, build plugins, fetch data, and handle onboarding and customer education.

 

But here's the thing—should you care?

 

I think you should, because we're going to see more and more of these things. And whether you like it or not, for a lot of people, this is going to be the way they interact with geospatial data. I don't think we can put the genie back in the bottle. I personally, I'm not entirely sure I would if I could.

 

Yeah, sure, there's a lot of uncertainty around what these things can do and how they're going to impact us. I get that. I feel it too. But we can't afford to stick our heads in the sand and pretend like it's not happening.

 

In this conversation, Jonathan walks through why he built Scribble (spoiler: his wife was expecting and he needed to solve an onboarding problem), the real risks of adding AI to your product, and the technical decisions behind using Gemini over OpenAI. We also talk about privacy concerns, the Model Context Protocol (MCP), and what this all means for the future of GIS.

 

We touch on the QGIS MCP server, the democratization of mapping tools, and when maps aren't actually the answer. It's an honest look at where we are with AI agents in geospatial, from someone who's actually building one.

 

https://en.wikipedia.org/wiki/Lojban 

 

https://github.com/jjsantos01/qgis_mcp 

 

How's that?

Exploring the Evolution and Impact of Mapillary with Ed from Meta. 

Topics include Ed's journey with Mapillary, the process of uploading and utilizing street-level imagery, and the integration with OpenStreetMap.

Ed talks about the challenges of mapping with various devices, the role of community contributions, and future potentials in mapping technology, such as using neural radiance fields (NeRFs) for creating immersive 3D scenes.

The episode provides insights into how Mapillary is advancing geospatial data collection and usage.

00:00 Introduction to the Map Scaping Podcast 00:57

Meet Ed: Product Manager at Meta 02:09

Ed's Journey with Mapillary 03:59

What is Mapillary? 07:00

The Evolution of 360 Cameras 09:20

Uploading Imagery to Mapillary 14:10

Building a 3D Model of the World 19:10

Meta's Use of Map Data 21:24

The Importance of Community in Mapping 24:15

The Importance of Authoritative Data 24:49

Meta's Contributions to Open Source Geo World 25:27

Real-World Applications: Vietnam's B Group 28:16

Innovative Mapping in Detroit 31:38

Future of Mapping: Lidar and Beyond 32:20

Exploring Neural Radiance Fields (NeRFs) 35:40

Challenges and Innovations in Mapping Technology 45:25

Community Contributions and Future Directions 46:37

Closing Remarks and Contact Information

 

Previous episodes that you might find interesting

https://mapscaping.com/podcast/scaling-map-data-generation-using-computer-vision/

https://mapscaping.com/podcast/the-rapid-editor/

https://mapscaping.com/podcast/overture-maps-and-the-daylight-distribution/

 

 

 

Telematics Data is Reshaping Our Understanding of Road Networks

In this episode MIT Professor Hari Balakrishnan explains how Cambridge Mobile Telematics (CMT) is transforming traditional road network analysis by layering dynamic behavioural data onto static map geometries. 

Telematics data creates "living maps" that go beyond traditional road geometry and attributes. By collecting movement data from 45 million users through phones and IoT devices, CMT has developed sophisticated models that can:

- Generate dynamic risk maps showing crash probability for every road segment globally - Detect infrastructure issues that aren't visible in traditional mapping (like poorly placed bus stops) - Validate and correct map attributes like speed limits and lane connectivity - Differentiate between overpasses and intersections using movement patterns - Create contextual understanding of road segments based on actual usage patterns

Particularly interesting for GIS professionals is CMT's approach to data fusion, combining traditional map geometry with temporal movement data to create predictive models. This has practical applications from infrastructure planning to autonomous vehicle navigation, where understanding the cultural context of road usage proves as important as precise geometry.

The episode challenges traditional static approaches to road network mapping, suggesting that the future lies in dynamic, behavior-informed spatial data models that can adapt to changing conditions and usage patterns.

For anyone working with transportation networks or smart city initiatives, this episode provides valuable insights into how movement data is changing our understanding of road infrastructure and spatial behaviour.

 

Connect with Hari on LinkedIn!

https://www.linkedin.com/in/hari-balakrishnan-0702263/

Cambridge Mobile Telematics

https://www.cmtelematics.com/

 

BTW,  I keep busy creating free mapping tools and publishing them there

https://mapscaping.com/map-tools/ swing by and take a look!

 

In this week’s episode, I’m thrilled to welcome back Ariel Seidman, founder of HiveMapper. Ariel was my very first podcast guest back in 2019, and HiveMapper has come a long way since then!

We explore how HiveMapper has evolved from a drone-based mapping system to a cutting-edge platform collecting street-level data at a global scale. Ariel shares the challenges of scaling large-scale mapping efforts, the pivot to building their own hardware, and the role of blockchain-based incentives in driving adoption.

Here are just a few topics we cover:

• Why HiveMapper shifted focus from drones to street-level mapping.
• The power of combining hardware and software to solve mapping challenges.
• How HiveMapper has already mapped 28% of the global road network.
• The revolutionary edge computing and data filtering techniques driving efficiency.
• What it takes to compete with industry giants like Google Maps.

Whether you're fascinated by the intersection of geospatial technology and innovation or looking for insights into scaling impactful startups, this episode is packed with value.

Let me know your thoughts or hit reply if you’d like to discuss the episode!

 

https://beemaps.com/

Connect with Ariel here https://www.linkedin.com/in/aseidman/

 

PS

I have just finished creating a web-based tool that lets you explore and download OpenStreetMap data, It is a bit different from other tools and I would appreciate some feedback. 

https://mapscaping.com/openstreetmap-category-viewer/ 

 

 

 

Tracking elephants in Southern Africa’s Kavango-Zambezi (KAZA) region, the largest transfrontier conservation area in the world.

Lead scientist Robin Naidoo from the World Wildlife Fund-US explains the complex, cross-border collaboration required to understand elephant movements across vast landscapes and the role of GNSS.

 

Connected with Robin 

https://www.worldwildlife.org/experts/robin-naidoo

 

Read more information about this study here

https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.14746   https://news.mongabay.com/2024/09/jumbo-collaring-effort-reveals-key-elephant-movement-corridors/   Check out https://www.movebank.org/

Today's episode touches on some pretty big topics like Imposter Syndrome, Mentorship, Career Progression, Adaptability and Diversity

Today you are going to hear two stories from two very different voices. Two brilliant people who happen to be women in geospatial. 

 

Ta Taneka

https://www.linkedin.com/in/ta-taneka/

Mary Murphy

https://www.linkedin.com/in/mary-murphy-12319433/

 

You can check out the GIS Directions Podcast here:

https://esriaustralia.com.au/gis-directions-podcast

or search for GIS Directions where every you listen to podcasts

 

Recommended Podcast Episodes

Getting where you want to go in your geospatial career

Mentorship leadership and career advice

Mentorship leadership and career advice

 

 

 

In this episode, Marco Bernasconi, co-founder and CEO of OPENGIS.ch, introduces us to QField, an open-source mobile application designed for field data collection in conjunction with QGIS.

Marco shares his journey in developing QField and discusses its seamless integration with QGIS, allowing users to capture, survey, and manage geospatial data on various mobile devices.

We also discuss the technical aspects of QField, including its user-friendly interface, the ability to connect with external sensors, and the recent introduction of QField Cloud for enhanced data synchronization and management.

Marco highlights the application’s diverse use cases, from citizen science initiatives to archaeological documentation and utility inspections, demonstrating its potential to transform data collection processes across various industries.

More information on Qfield: 

https://qfield.org/

https://qfield.cloud/

Or https://www.opengis.ch/#contact 

 

 

On a personal note, I have been working as a freelance Geospatial consultant for some time now and one of my projects is slowly winding down, which is why I am looking for new projects to get involved in!

If you need expertise in Geospatial consultancy, GIS management or the marketing of geospatial products and services Please reach out!

https://www.linkedin.com/in/danielodonohue/

or contact me here info@mapscpaing.com

 

This is the story of Priscilla Cole, and what she did when she discovered that her ambitions were bigger than the tools she was using!

Connect with Priscilla here!

https://www.linkedin.com/in/priscilla-cole-5892549/

 

Recommended Listening

The Way You Talk About Your Skills Is Costing You Money

 

Geospatial Consulting As A Business And Career

 

Mid-Life Career Change

 

Getting Where You Want To Go In Your Career

 

Applying For A Job, Getting Picked and Negotiating

 

Mentorship Leadership And Career Advice

 

In this episode, I'm joined by Konstantine Klemmer, a researcher at Microsoft, to dive deep into the fascinating world of GeoAI. Konstantine introduces us to Satclip, a cutting-edge model that encodes geographic locations based on satellite images.

We discuss how Satclip works, the data it uses, and its potential applications, particularly in low-resource settings and predictive modeling. Whether you're into AI, geography, or just curious about the intersection of these fields, this episode is packed with insights.

Key Takeaways:

• What is Satclip?: Learn about Satclip's location encoding, a neural network that converts geographic coordinates into numerical representations based on satellite images.
• Data and Training: Understand how Satclip is trained using Sentinel-2 satellite images and how it captures unique geographic features.
• Applications: Discover how Satclip can be used in low-resource environments, such as on edge devices, and how it enhances other models by providing geographic context.
• The Future of GeoAI: Explore the potential future directions for Satclip, including more detailed regional models and the integration of multiple data modalities.

Connect with Konstantine https://www.linkedin.com/in/konstantinklemmer/

Try Satclip https://github.com/microsoft/satclip

 

Recommended Listening

https://mapscaping.com/podcast/computer-vision-and-geoai/

https://mapscaping.com/podcast/planet-imaging-everything-every-day-almost/

 

 

 

 

In this episode, I welcome Jason Gilman, a Principal Software Engineer at Element 84, to explore the exciting world of natural language geocoding.

Key Topics Discussed:

1. Introduction to Natural Language Geocoding:

   • Jason explains the concept of natural language geocoding and its significance in converting textual descriptions of locations into precise geographical data. This involves using large language models to interpret a user's natural language input, such as "the coast of Florida south of Miami," and transform it into an accurate polygon that represents that specific area on a map. This process automates and simplifies how users interact with geospatial data, making it more accessible and user-friendly.

2. The Evolution of AI and ML in Geospatial Work:

   • Over the last six months, Jason has shifted focus to AI and machine learning, leveraging large language models to enhance geospatial data processing.

3. Challenges and Solutions:

   • Jason discusses the challenges of interpreting natural language descriptions and the solutions they've implemented, such as using JSON schemas and OpenStreetMap data.

4. Applications and Use Cases:

   • From finding specific datasets to processing geographical queries, the applications of natural language geocoding are vast. Jason shares some real-world examples and potential future uses.

5. Future of Geospatial AIML:

   • Jason touches on the broader implications of geospatial AI and ML, including the potential for natural language geoprocessing and its impact on scientific research and everyday applications.

Interesting Insights:

• The use of large language models can simplify complex geospatial queries, making advanced geospatial analysis accessible to non-experts.
• Integration of AI and machine learning with traditional geospatial tools opens new avenues for research and application, from environmental monitoring to urban planning.

Quotes:

• "Natural language geocoding is about turning a user's textual description of a place on Earth into a precise polygon."
• "The combination of vision models and large language models allows us to automate complex tasks that previously required manual effort."

Additional Resources:

• Element 84 Website
• State of the Map US Conference Talk on YouTube
• Blog Posts on Natural Language Geocoding

Connect with Jason:

• Visit Element 84's website for more information and contact details.
• Google "Element 84 Natural Language Geocoding" for additional resources and talks.

This podcast episode is all about semantic search and using embeddings to analyse text and social media data.

Dominik Weckmüller, a researcher at the Technical University of Dresden, talks about his PhD research, where he looks at how to analyze text with geographic references. 

He explains hyperloglog and embeddings, showing how these methods capture the meaning of text and can be used to search big databases without knowing the topics beforehand.

Here are the main points discussed:

1. Intro to Semantic Search and Hyperloglog: Looking at social media data by counting different users talking about specific topics in parks, while keeping privacy in mind.
2. Embeddings and Deep Learning Models: Turning text into numerical vectors (embeddings) to understand its meaning, allowing for advanced searches.
3. Application Examples: Using embeddings to search for things like emotions or activities in parks without needing predefined keywords.
4. Creating and Using Embeddings: Tools like transformers.js let you make embeddings on your computer, making it easy to analyze text.
5. Challenges and Innovations: Talking about how to explain the models, deal with long texts, and keep data private when using embeddings.
6. Future Directions: The potential for using embeddings with different media (like images and videos) and languages, plus the ongoing research in this fast-moving field.

Connect with Dominik Weckmüller here https://geo.rocks/

Stay up to date with AI here

https://huggingface.co/ Try searching for “map”  here https://huggingface.co/spaces

 

Check out this project I am working on 

https://quickmaptools.com/

 

In this episode, we welcome back Lauren Guy, CEO and founder of ASTERRA, a groundbreaking company using L band and synthetic aperture radar (SAR) for commercial purposes. Lauren shares his journey as a geophysicist and discusses the innovative applications of L band in detecting water leakages, soil moisture, and even minerals from space. Dive deep into the technical, commercial, and environmental aspects of SAR technology and learn about the future potential of this exciting field.

**Key Topics Covered:**

1. **Introduction to Astera**:

   - Overview of Asterra's mission and Lauren Guy's background as a geophysicist.

   - The unique use of L band and SAR for commercial applications.

 

1. **Understanding L Band and Synthetic Aperture Radar (SAR)**:

   - Explanation of the electromagnetic spectrum and how L band fits in.

   - Advantages of L band, including its ability to penetrate the ground.

 

1. **Technical Insights into SAR**:

   - Detailed discussion on polarizations, signal processing, and the electrical properties of materials detected by SAR.

   - Comparison between L band and other bands like X and C band.

 

1. **Applications and Benefits of L Band**:

   - Real-world examples of how Astera uses L band for water leak detection and soil moisture mapping.

   - Discussion on the environmental and commercial impact of these applications.

 

1. **Challenges and Limitations**:

   - Addressing issues such as noise interference from cell phones and radars.

   - Limitations in resolution and the complexities of SAR technology.

 

1. **Case Studies and Success Stories**:

   - Success stories including the detection of 118,000 water leakages worldwide and the discovery of significant lithium deposits.

 

1. **Business Strategies and Market Penetration**:

   - Insights into ASTERRA's business model, customer education, and market challenges.

   - Strategies for overcoming barriers and building trust with clients.

 

1. **Future Aspirations and Technological Developments**:

   - Plans for launching their own satellites to ensure reliable data sources.

   - The role of AI in enhancing SAR capabilities and improving detection accuracy.

 

1. **Entrepreneurial Advice for Remote Sensing Practitioners**:

   - Lauren’s advice for remote sensing scientists and entrepreneurs in the industry.

   - The importance of data feedback loops and the journey from a 20% to an 86% success rate in detections.

 

**Guest Information:**

- **Lauren Guy**: CTO and founder of ASTERRA. Connect with Lauren on https://www.linkedin.com/in/lauren-guy-asterra/

 

**Company Information:**

- **ASTERRA**: Learn more about ASTERRA’s innovative solutions at https://asterra.io/

 

**Additional Resources:**

- Check out Lauren’s previous appearance on the podcast for more insights into SAR technology.

- Explore ASTERRA’s groundbreaking work in remote sensing and their various applications across different industries.

 

**Episode Highlights:**

- "We can find water leakages from space and distinguish treated water from other types of water based on their dielectric properties."

- "ASTERRA has verified, dug, and fixed 118,000 leakages across 65 countries using L band SAR technology."

- "Our success rate has increased from 20% to around 86% thanks to the integration of AI and continuous data feedback."

 

**Support the Show:**

- If you enjoyed this episode, please leave a review on your favourite podcast platform and share it with your network.

 

Thank you for tuning in to the MapScaping Podcast!

 

Recommended Listening 

Finding Water Leaks From Space

Introduction To Synthetic Aperture Radar-SAR

Flood Monitoring From Space ( using SAR)

 

Cloud-native geospatial, range requests, chucks, COGs and COPCs ... [ insert confusing acronym here ] 

Sometimes It feels like we need to learn a whole new vocabulary and if you have been doing #geo for a while you might be wondering how much of this is actually going to impact me. What bits of this are the ones that I need to know about? 

I don’t think that anyone is going to be talking about cloud native in 10 years, in the same way, no one talks about digital cartography or computer analysis because where else would you do your cartography? And how else would you do your analysis? 

Maybe the names won’t be as important but the concepts will and while this episode is focused on Geoparquet it does so within the context of cloud-native geospatial - and this concept is not going away! 

 

You can connect with Kyle Barron here https://x.com/kylebarron2 or here https://kylebarron.dev/ 

 

 If you want to learn more about cloud-native geospatial I can highly recommend these episodes

 

https://mapscaping.com/podcast/cloud-optimized-point-clouds/

https://mapscaping.com/podcast/introduction-to-cloud-native-geospatial/

https://mapscaping.com/podcast/planet-scale-tiled-maps-without-a-server/

https://mapscaping.com/podcast/what-is-modern-gis/

https://mapscaping.com/podcast/the-planetary-computer/

 

I am working on a new project called https://quickmaptools.com/ like the name suggests it is a bunch of browser-based map tools. So far we have created several different conversion tools and will continue to add more to the list. Check it and let me know what you think!

 

Mappedin started as a school project and evolved into a leading indoor mapping company, working with malls, airports, hospitals, and Fortune 500 companies.

 

You guessed, today's podcast is all about indoor mapping, why it's hard, what are the use cases driving it, what the state of the art looks like today and what we can expect in the future.

 

Key points discussed include:

1. **The Challenge of Indoor Mapping**: Unlike outdoor mapping, indoor environments are complex due to the density of objects and frequent changes. Moreover, indoor spaces are mostly private property, making it difficult to collect data comprehensively.

2. **Importance of Indoor Maps**: Despite the challenges, the need for indoor maps is growing. Applications range from wayfinding in malls and airports to optimizing logistics in warehouses and creating better guest experiences in various venues.

3. **Mappedins's Approach**: enabling non-experts to create and maintain indoor maps. Their tools are designed for everyday users, allowing them to update maps as easily as they would a document in Google Docs.

4. **Technological Advances**: While technologies like LiDAR and digital twins offer detailed 3D models, are they really practical? .

5. **Indoor Positioning**: Accurate indoor positioning is crucial for the success of indoor maps, similar to how GPS revolutionized outdoor mapping. However, this remains a challenging area due to signal interference and the complexity of indoor spaces.

6. **Future Outlook**:  Digital indoor maps become as ubiquitous as Wi-Fi, providing essential data for various applications and improving overall user experience ... but we are not there yet!

Try Mappedin for yourself https://www.mappedin.com/

or connect with Hongwei  here  https://www.linkedin.com/in/hongweil/ 

 

Recommended Listening 

Where does the blue dot come from ( how Google knows your location )

Hyper Accurate Indoor Location

Using the Geomagnetic fields of buildings to navigate indoors

 

I am working on a new project https://quickmaptools.com/ and would really appreciate some feedback! 

 

 

 

 

 

 

Hugo Powell, from immap.org shares his expert insights on how GIS technologies are leveraged to analyze data, visualize scenarios, and facilitate rapid decision-making during emergencies.

Here are the key tools mentioned:

1. **Kobo Toolbox**: An open-source tool used for data collection in humanitarian contexts. Kobo Toolbox allows for both quantitative and qualitative data collection and is operational offline, which is crucial in areas with limited internet connectivity. It supports geospatial data collection and can be used for needs assessments in settings like refugee camps.

2. **ODK Collect**: Similar to Kobo Toolbox, ODK Collect is an open-source mobile application used for field data collection. It is widely used in humanitarian efforts for its ease of use and the capability to work offline.

3. **QGIS**: A free and open-source geographic information system used for viewing, editing, and analyzing geospatial data. Hugo notes that QGIS is core for mapping and data analysis in humanitarian operations.

4. **Tableau and Power BI**: Business intelligence tools mentioned for their use in analyzing and visualizing data. These tools help in making data-driven decisions during humanitarian operations.

5. **Humanitarian Data Exchange (HDX)**: An open platform for sharing data across crises and organizations, which helps in avoiding duplication of efforts and enhances coordination among humanitarian actors.

6. **Humanitarian OpenStreetMap Team (HOT)**: Provides crowdsourced geospatial data which is extremely valuable in humanitarian settings for its accuracy and timeliness.

7. **Esri’s Living Atlas and other Esri tools**: While not open-source, Esri’s tools are sometimes used for their comprehensive geospatial data, particularly in natural disaster contexts like earthquakes.

8. **Humanitarian Spatial Data Center**: Managed by IMAP, this tool aggregates and processes data, providing access to data, analytics, and visualization tools all in one place. It has been particularly successful in deployments like Afghanistan.

 

This episode was sponsored by scribblemaps.com

https://youtu.be/CDkG9eS6H2M

 

Recommended Listening 

Geospatial Support For Humanitarian Emergencies

A Self-Contained Environment For Open-Source Geospatial Tools

GGIS Offline And In The Field

The Business Of Web Maps

Peer to Peer Mapping And Digital Democracy

 

I am working on a new project over at QuickMapTools.com

and any feedback is really appreciated! 

 

 

AI Autocomplete for QGIS

Brendan Ashworth the CTO and co-founder of https://buntinglabs.com/ focuses on integrating AI with QGIS, and today on the podcast we are talking about Autocomplete for vectorization.

Along the way Brendan will share with us why Bunting Labs chose to build this on top of QGIS, the Challenges in Map Digitization, what the development process was like and how this is different from tools like Segment Anything ( from meta ) 

Here's what we discussed:

1. Introduction to Bunting Labs: Get to know more about Brendan and Bunting Labs, whose mission revolves around enhancing QGIS with AI, especially focusing on automating vectorization processes.

2. AI Autocomplete for Vectorization: We explored the AI autocomplete feature developed by Bunting Labs that simplifies the vectorization of maps in QGIS, streamlining the digitization process for better efficiency.

3. Brendan’s Background and Motivation: Brendan shared his journey from a software engineer to a pivotal player in the geospatial sector, spurred by a project that showcased the potential of merging geospatial data with machine learning.

4. Why Choose QGIS?: Discover why Bunting Labs opted for QGIS over other GIS platforms, with an emphasis on its open-source nature and vibrant community ecosystem.

5. Challenges in Map Digitization: Our conversation covered the technical challenges involved in developing AI capable of accurately understanding and digitizing maps.

6. Iterative Development and Learning: Brendan highlighted the evolutionary process of their AI model, which has significantly improved from its early versions.

7. AI vs. Segment Anything: Brendan explained how their AI autocomplete tool differs from existing solutions like Segment Anything, particularly in handling specific digitizing challenges.

8. The Future of AI in Geospatial Data Analysis: We discussed potential future applications of AI in geospatial data, including automatic georeferencing and metadata extraction.

9. Privacy Considerations: We also touched on the importance of privacy in the development and deployment of AI technologies in geospatial data analysis.

10. Changing the Geospatial Landscape: Brendan shared his vision for using geospatial data not just to map the current world but to plan and improve future landscapes.

Sponsored by https://www.scribblemaps.com/

Recommended Listening

https://mapscaping.com/podcast/the-business-of-web-maps/

https://mapscaping.com/podcast/the-business-of-qgis-development/

https://mapscaping.com/podcast/qgis-offline-and-in-the-field/

https://mapscaping.com/podcast/computer-vision-and-geoai/

 

https://quickmaptools.com/ - MapTools to save your time processing GIS data

 

GNSS receivers - why precise positioning will not be coming to your phone any time soon

 

Igor is the CEO and cofounder of Emlid.com a company that started out making high-precision GNSS receivers in his kitchen and crowd-funded the first batch on Kickstarter. 

 

But that was over ten years ago so today on the podcast Igor is going to tell us about the innovations that made this possible, give a great explanation of RTK and PPP and explain why we should expect to see high precision positioning on your phone any time soon.

Connect with Igor here: https://www.linkedin.com/in/igor-vereninov-52a73ab0/

Or visit https://emlid.com/

 

In this episode, we cover: 

1. **Introduction to Emlid and its Focus**: Introduction to the company Emlid, its CEO and co-founder Igor, and their focus on high precision GNSS receivers and software designed for centimeter accuracy positioning.

 

1. **Startup Story and Crowdfunding**: The origin story of Emlid, starting from working with drones in university, the need for accurate maps, the initial challenges with high precision GPS technology, the development of their own GNSS receiver, and their successful crowdfunding campaign on Kickstarter.

 

1. **Innovations in GNSS Technology**: How Emlid managed to make high precision GNSS technology more affordable and accessible, the role of open-source software, and the technical innovations that allowed them to reduce the cost and size of GNSS receivers.

 

1. **Market and Technology Evolution**: The discussion on how the market for GNSS technology has evolved, including the impact of autonomous cars on the development and availability of multi-frequency GNSS chips, and how these advancements benefited broader applications beyond surveying and construction.

 

1. **Precision vs. Accuracy in GNSS**: An explanation of the difference between precision and accuracy in the context of GNSS technology, and the significance of each in various applications like drone mapping and volume measurements.

 

1. **RTK and PPP Explained**: A detailed explanation of Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), including how they work, their applications, and their advantages and limitations.

 

1. **The Future of GNSS Technology**: Insights into the future directions of GNSS technology, including the challenges and potentials for achieving sub-centimeter accuracy with smartphones, the complementarity of GNSS and visual positioning systems, and the potential for GNSS technology to replace traditional surveying methods.

 

1. **Global Utility of GNSS**: A discussion on the importance of GNSS as a global utility, its indispensable role in modern technology and everyday life, and the potential consequences of GNSS failures.

More GNSS-related podcast episodes!

From GNSS to VPS

Reimaging GPS

How Google Knows Your Location

Past, Present and Future of GNSS

SBAS - A base station in the sky

 

 

 

 

 

Refactoring the Way you Talk About your geospatial skills: It is Costing you Money

Some of the key topics in this episode 

1.Our Geospatial Skills and Marketability: There's a realization that while our traditional geospatial skills are valuable, they might not always be marketed effectively to match the broader IT and data analysis job markets. We discuss the benefit of framing our skills in terms that are more widely recognized outside the niche of geospatial technology, such as data science or IT.

2.The Spatial Discount: We explore the concept of the spatial discount, which refers to the observation that geospatial professionals might face a disparity in compensation compared to their counterparts in more generalized IT roles, despite having highly transferable and valuable data manipulation skills.

3. Skill Development and Adaptation: The importance of continually developing skills that are not only advanced within the geospatial domain but also marketable across various sectors is emphasized. Learning and mastering technologies that have broad applications, such as SQL for spatial data manipulation, can enhance our versatility and marketability.

4. Communication and Marketing Skills: Our ability to effectively communicate and market our skills is highlighted as crucial for career advancement. We are encouraged to adopt the language and terminology that resonate with broader industries and potential employers, moving beyond the jargon of the geospatial field.

5. Finding Value in Our Geospatial Work: The discussion also touches on the importance of identifying and articulating the real-world value of our geospatial work. We should focus on how our skills can solve practical problems and address the needs of businesses and organizations, rather than solely on the technical complexity of our tasks.

6. Professional Development: Lastly, the conversation advocates for a proactive approach to our professional development, suggesting that we should seek out opportunities to learn new skills and technologies that align with market demands and personal interests.

These points collectively suggest a strategy for us, as geospatial professionals, to enhance our career prospects: by broadening our skill sets, effectively marketing our capabilities, and aligning our work with the needs and language of the wider IT and data analysis fields.

 

Connect with Brain Timoney on LinkedIn 

Thank you to our sponsors 

https://www.scribblemaps.com/ 

https://merginmaps.com/

 

Recommended Listening

 

Modern Geospatial

Rebranding Gis and Geospatial

Getting Where You Want To Go In Your Geospatial Career

Mid-Career Change

 

 

Modern geospatial - not the bleeding edge of geospatial but modern geospatial - what is it?

Well my guest Will Cadell, the CEO of SparkGeo describes modern geospatial as the intersection of the cloud, smart space, open source data/standards, AI and smart devices - that's modern geospatial 

And as you will hear during the discussion it's important to understand the difference between modernisation and innovation when we think about moving people from where they are now to where they want to be with regards to their geospatial capabilities. 

You might be wondering - what does any of this have to do with me? I just want to make better things, I just want to help people use all this awesome geospatial stuff …

but you don’t get to do that without first understanding what “better” looks like for them - what is their version of awesome geo stuff … and that is why you should listen to this episode! 

 

Connect with Will Cadell

Twitter https://twitter.com/geo_will

LinkedIn https://www.linkedin.com/in/willcadell/

SparkGeo  https://sparkgeo.com/

https://www.strategicgeospatial.com/

 

This episode is sponsored by https://www.scribblemaps.com/

 

Recommended Listening

 

The Business of Web Maps

https://mapscaping.com/podcast/the-business-of-web-maps/

 

Modern GIS

https://mapscaping.com/podcast/what-is-modern-gis/

 

 

 

 

 

 

 

 

The main topics discussed during this episode include:

1. Basics of LIDAR data and its applications.
2. Differences between LIDAR and photogrammetry.
3. Processing chain of LIDAR data.
4. Challenges in classifying point clouds.
5. Applications of LIDAR technology in vegetation mapping, terrain modelling, and infrastructure inspection.
6. The future of LIDAR technology includes the potential for more affordable and widespread use.
7. Importance of automated processing tools for handling large volumes of data.

Connect with Nejc Dougan here: https://www.linkedin.com/in/nejcdougan/

or at https://www.flai.ai/ 

 

Recommended listening 

 

Cloud Optimized Point Clouds

https://mapscaping.com/podcast/cloud-optimized-point-clouds/

 

PDAL - the point data abstraction library

https://mapscaping.com/podcast/pdal-point-data-abstraction-library/

 

Lidar from drones

https://mapscaping.com/podcast/lidar-from-drones/

 

Bathymetric Lidar

https://mapscaping.com/podcast/bathymetric-lidar-and-blue-carbon/