In this tutorial, you will find my most thorough GeoAI workflow yet, using Googleโs ๐๐ฅ๐ฉ๐ก๐๐๐๐ซ๐ญ๐ก for urban data science.
We will start by downloading the AlphaEarth data directly in Python, without any third-party tools, and then combine it with OpenStreetMap building footprints.
Quick recap: Google AlphaEarth is Googleโs brand-new GeoAI Earth observation initiative that combines petabytes of satellite imagery and remote sensing to present large-scale, global, 10x10m-resolution multispectral (64 bands for starters) data.
Leveraging this kind of data, due to its small-scale spatial resolution and high feature variance, it's probably going to be a game-changer for data-driven urban planning. It allows data scientists and city planners alike to characterize urban-scale areas all around the world with high precision and harness a rich set of features for planning, predictive modeling, and many other applications - especially if combined with the right vector data.
To get to the point, in this tutorial, you will
- Learn how to acquire and explore AlphaEarth data in Python
- Match that data to real buildings,
- Visualize the results through interactive Folium maps, and
- Build a simple binary building classification model using GeoPandas, Earth Engine, and scikit-learn.
Code: https://open.substack.com/pub/milanjanosov/p/building-classification-with-geoai?r=3o5qdz&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
๐๐จ๐จ๐ ๐ฅ๐ ๐๐ฅ๐ฉ๐ก๐๐๐๐ซ๐ญ๐ก: https://blog.google/technology/ai/google-earth-ai/
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