The SparkGeo team has tested Lovable and asked it to create a simple interactive map showing Canadian cities and their population.
results weren’t always consistent. Running the same prompt twice sometimes yielded drastically different outcomes: one time clean and functional, the next time oddly styled or partially broken. In one case, even after explicitly stating not to use any frameworks, Lovable still generated a React-based implementation. A quick follow-up prompt corrected this, and to its credit, the revised output was clean, readable vanilla JavaScript.
That responsiveness is promising, but also highlights an important caveat: some knowledge of development (especially in geospatial UI) is still necessary. Without it, spotting issues or debugging quirks could become a frustrating barrier. Lovable gets you 80% of the way there, but that final 20% – the part where accuracy, accessibility, and usability matter most, still depends on human expertise.
Even for simple applications, you need a knowledgeable engineer to create a finished product. This is fine if you treat generative AI assistants as a tool, not the solution.
But an important question lingers. While engineers rely more and more on generative AI to build applications, they spend less time understanding the concepts and libraries they’re working with, making mistakes and gaining experience allowing them to understand and improve the code they write. By extension, engineers never learn enough about the technology they’re putting to work, maybe even unlearn some of the things they know now. If that turns out to be the case, who will maintain applications largely built with AI in the future?
I’d love to see a similar interface for generating GDAL commands; Cameron Kruse:
Tippecanoe has great documentation in its ReadMe with all the info you need to get started, but I’ve often found that half the battle is finding the commands, deciding which ones to use and stringing them together into a coherent command you can actually run in your terminal. I’ve made this a little easier by taking many of the Tippecanoe features and converting them into an interface you can use to generate your commands. The premise of this tool is you select all the options you want from the dashboard and a command is generated below you can copy and paste in your terminal.
The good folks at Heigit have released ohsome-planet, a handy tool to turn OpenStreetMap history data from PBF into GeoParquet files, ready to use in common GIS applications.
Working with raw OSM data presents several challenges due to its complex structure. Typically, users require data that is readily compatible with Geographic Information System (GIS) applications. Our new tool streamlines this process, providing a structured and GIS-ready dataset for improved usability.
The tool also enriches OSM element data by integrating information from OSM changesets and administrative boundaries. This additional contextual data allows for more efficient and straightforward spatial analysis, further improving the utility of OSM datasets.
The tool is written in Java and you have to build it yourself; a small price to pay for more easily accessible free and open data.