QGIS Autocomplete Featured on Mapscaping Podcast

Brendan Ashworth

April 16, 2024

I recently had the pleasure of joining Daniel O'Donohue from the Mapscaping Podcast to discuss our AI Autocomplete tool for vectorization in QGIS, our AI georeferencer, and other GIS technologies we've been building. I wanted to take this opportunity to share some of my thoughts from our conversation and invite you to listen to the episode to dive deeper into how we’re enhancing GIS with AI.

You can find the QGIS podcast episode here or on Apple Podcasts, Spotify, or wherever you usually get your podcasts.

What we cover

During the podcast, I discussed why we chose QGIS over other platforms, emphasizing its open-source nature—something that fits into my strengths as a developer—and the vibrant community that supports it. These elements make QGIS an ideal candidate for quickly testing new software with real professionals in their GIS, allowing us to easily iterate on our software.

What is the AI Autocomplete Plugin?

Our discussion on the podcast centered around our AI Autocomplete feature, a tool designed to simplify and streamline the digitization of maps. This tool is not just about automation; it’s about transforming the map digitization process to make it more efficient and accurate. This is crucial for professionals who rely on precise and timely geospatial data.

One of the key challenges in map digitization that we tackled was developing an AI capable of understanding and interpreting complex map features accurately. This required a robust iterative development process, where we learned and improved the tool progressively. It’s a journey that has shown significant advancements from its early versions, and I shared these learnings and the evolutionary steps of our technology on the podcast.

How is our AI different from other AI models?

We also discussed how our AI Autocomplete stands apart from other tools like Meta's Segment Anything. Our tool is specifically designed to meet the unique demands of the geospatial sector, allowing LineString/MultiLineString/Polygon features to be drawn directly on a raster.

What's next for GeoAI?

Looking ahead, we talked about the future of AI in geospatial data analysis, such as automating georeferencing and extracting metadata. These advancements could lead to substantial improvements in how quickly and efficiently geospatial data can be converted into actionable insights, making use of AI tools.

Why do we want to build GeoAI?

Lastly, we touched upon how these technological advancements are not just about understanding our world better but also about planning and improving future landscapes. At Bunting Labs, we are excited about the possibilities that lie at the intersection of AI and GIS, and what that might mean for the world.

Give the episode a listen

Whether you are a GIS professional, a tech enthusiast, or someone curious about the future of AI and mapping, there’s something in the podcast for everyone in the geospatial community. Let’s map the future together!

Listen to the full podcast episode here