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

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: Learn Python. AI coding is constantly improving and worth thinking about. Start thinking about maps as experiences. Embrace the Cloud and understand cloud-native formats. Standards matter. 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