Geospatial Building Growth Analysis

Urban growth mapping using Google Open Buildings and H3 hex grids.

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🛰️ Geospatial Building Growth Analysis

A modular pipeline for extracting, analyzing, and visualizing urban growth patterns from satellite data.
Focus: Metropolitan Area of San Salvador (AMSS), El Salvador.

🚀 What does it do?

  • Maps year-on-year building growth (counts, area, and height) using the Google Open Buildings Temporal Dataset.
  • Uses H3 hexagonal grids for high-res spatial analysis (~100m).
  • Fully automated workflow: feature extraction, growth computation, and mapping.
Left: Building counts per H3 hex (2023).
Middle: Mean building heights across the city (2023).
Right: Animated top 1% growth cells from 2016 to 2023.

🔍 Key Features

  • Automated H3 grid generation over any AOI.
  • Batch feature extraction from Earth Engine (building count, area, height).
  • Year-on-year growth metrics per municipality and hex cell.
  • Customizable workflow: skip/re-run any step, override config via CLI.
  • High-res maps and animations for easy visualization.

💡 How does it work?

The workflow consists of modular Python scripts:

  • AOI and grid setup
  • Feature extraction via Google Earth Engine
  • Growth computation
  • Visualization (static and animated)

All driven by a master script for reproducibility.

📊 Outputs

  • Geopackages of extracted features and growth metrics
  • Publication-ready plots and GIFs
  • Easily extendable to any region covered by Open Buildings

🛠️ Technologies

  • Python (geopandas, h3, matplotlib, etc.)
  • Google Earth Engine API
  • H3 hexagonal indexing
  • Streamlined CLI & config for full control

🔗 GitHub Repo

Project lead: Javier Alfaro

Made with ❤️ for open urban analytics.