Python and Google Earth Engine workflows for detecting and classifying urban change using Google’s Open Buildings 2.5D Dataset, with a focus on informal settlements in Nairobi. Includes scripts for processing, typology classification, slum-level validation prep, and city-scale spatial analysis.

Efficient pipeline to download and extract building footprints from Google’s Open Buildings V3 dataset, using only tiles and geometries that intersect with your region of interest (ROI). Includes two Jupyter notebooks. Saves time, storage, and processing by avoiding the full 178 GB global dataset.

Bulk downloader for Google’s 2.5D Open Buildings dataset at national or multi-country scale. Bypasses Earth Engine limitations by using direct download links. Includes a Jupyter Notebook, URL files, and data description..

📬 Get Telegram messages when your Jupyter Notebook finishes running. A simple, secure, and useful setup for long-running tasks.