Braaten et al. - Bridging the gap between Earth Engine and the Scientific Python Ecosystem

Google Earth Engine contains ~100 petabytes of satellite data and adds ~1 petabyte monthly, making it a massive repository for geospatial analysis

The platform offers distributed computing capabilities, allowing users to process large datasets without downloading them locally or managing infrastructure

New developments have improved connectivity between Earth Engine and the Python ecosystem, enabling seamless integration with libraries like Pandas, GeoPandas, NumPy, and xarray

GEEMAP serves as a bridge between Earth Engine and Python, providing one-line code solutions for data visualization and analysis

Two main data catalogs exist:

• Main catalog (Earth Engine’s master catalog)
• Community catalog (user-contributed datasets)

Earth Engine provides ~250GB of storage space per project and handles complex tasks like:

• Data projection alignment
• Scale normalization
• Distributed processing

Users can process data directly in the cloud and export only the results, avoiding the need to download massive raw datasets

The platform supports both vector and raster data analysis with simple conversion between formats

Interactive visualization capabilities allow real-time exploration of large-scale environmental and geospatial data

The system works on a “pull basis” where instructions are sent to Google’s servers, processed across multiple nodes, and results are returned to the client