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

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

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

 

 

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

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

 

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

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

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

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

Some of the things we get into:

The difference between digital twins and 3D assets and why Duality treats twins as modular building blocks you compose into scenarios, rather than as one monolithic environment

How they build environments from the ground up using DEM data, satellite imagery, photogrammetry and biome catalogues and why building them this way means everything is annotated from the start

Calibrating virtual sensors against real ones, including synthetic aperture radar, and why sensor noise characteristics matter as much as physics

Predicting how a material will behave across the spectrum (infrared, SAR) just from its visual response — and when that prediction breaks down

Why "clutter" only becomes clutter once you know what you're looking for, and why it doesn't need to be perfect

Modelling star fields for localisation in space, where there are no roads or buildings to navigate by

Explicit versus generative world models, and why you need both

A project with AWS simulating emergency ambulance routing through a city, complete with autonomous vehicles, traffic control and teleoperated human agents

Where Duality is not the right tool molecular scale, virtual patients, drug discovery

And yes, a story about robotics companies renting Airbnbs, trashing them, and leaving

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

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

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

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

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

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

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

More at 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