The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography Podcast Por MapScaping arte de portada

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

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

De: MapScaping
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A podcast for geospatial people. Weekly episodes that focus on the tech, trends, tools, and stories from the geospatial world. Interviews with the people that are shaping the future of GIS, geospatial as well as practitioners working in the geo industry. This is a podcast for the GIS and geospatial community subscribe or visit https://mapscaping.com to learn moreCopyright 2019 All rights reserved. Ciencia Ciencias Geológicas Historia Natural Naturaleza y Ecología
Episodios
  • I have been making AI slop and you should too

    I have been making AI slop and you should too
    Nov 17 2025
    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 ...
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    19 m
  • Scribble: An AI Agent for Web Mapping

    Scribble: An AI Agent for Web Mapping
    Nov 10 2025

    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?
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    49 m
  • Mapillary

    Mapillary
    Oct 27 2025

    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/
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    48 m
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