Google Cloud introduced Earth Engine in BigQuery, combining vector and raster analytics for advanced geospatial analysis
It supports raster data, such as satellite imagery, which encodes geographic information as pixel grids with values like temperature or land cover
A new geography function in BigQuery enables users to extract aggregate statistics (mean, min, max, etc.) from raster data within specified geographic boundaries
Formerly Analytics Hub, BigQuery Sharing now includes Earth Engine datasets, offering access to ready-to-analyze data for various geospatial use cases
Use cases include analyzing wildfire risk, flood mapping, and drought conditions for urban planning and infrastructure applications
Evaluate land use, elevation, and cover categories for agricultural assessments and supply chain management, ensuring commodities are sourced sustainably
Methane Emissions Monitoring: Identify methane emission hotspots using datasets like MethaneSAT L4 Area Sources to support mitigation efforts
Custom Raster Data: Users can import their own raster datasets from Earth Engine in BigQuery image assets or Cloud Storage GeoTIFFs
ST_RegionStats() is billed under BigQuery Services SKU, with costs influenced by input rows, raster quality, geography complexity, and pixel size
The include argument in ST_RegionStats() allows users to assign pixel weights (0 to 1) for more precise calculations