Google Buildings V3 is a powerful dataset, but at 178 GB, it's not easy to work with.
In this blogpost, we introduce a two-step Python workflow to efficiently download and extract only the building footprint data you need — using your own region of interest. No need to process the world — just get the tiles that matter, filter them, and save clean GeoPackages. Ideal for urban research, slum mapping, or large-scale city analysis. Lightweight, scalable, and open-source.
Tired of waiting for Jupyter cells to finish? In this post, we show how to use a simple Telegram bot to get instant notifications when your notebook completes — no email setup, no complex libraries. A quick, beginner-friendly solution for long-running geospatial or ML tasks.
A lightweight, offline-friendly tool for downloading Google’s 2.5D Open Buildings dataset at national scale. This blog introduces a Jupyter Notebook that bypasses Earth Engine limits by using direct download links—enabling researchers to work with high-resolution building data across entire countries. Developed under the DEPRIMAP project.
DEPRIMAP is a research funded by FORMAS (Swedish Research Council, application 2023-01210) involving KAU (Karlstad University, Sweden)
