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

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?