
AI Autocomplete for QGIS
The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geographyShow Notes
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:
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.
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.
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.
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.
Challenges in Map Digitization: Our conversation covered the technical challenges involved in developing AI capable of accurately understanding and digitizing maps.
Iterative Development and Learning: Brendan highlighted the evolutionary process of their AI model, which has significantly improved from its early versions.
AI vs. Segment Anything: Brendan explained how their AI autocomplete tool differs from existing solutions like Segment Anything, particularly in handling specific digitizing challenges.
The Future of AI in Geospatial Data Analysis: We discussed potential future applications of AI in geospatial data, including automatic georeferencing and metadata extraction.
Privacy Considerations: We also touched on the importance of privacy in the development and deployment of AI technologies in geospatial data analysis.
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
More from The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography
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:
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:
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.
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: