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Expanding BigQuery geospatial capabilities with Earth Engine raster analytics
- Google Cloud introduced Earth Engine in BigQuery, allowing advanced geospatial analytics using SQL.
- Earth Engine excels at raster data, while BigQuery is efficient with vector data, making them a powerful combination.
- Key features of Earth Engine in BigQuery include the ST_RegionStats() function and access to Earth Engine datasets.
- The ST_RegionStats() function allows efficient extraction of statistics from raster data within specified geographic boundaries.
- Five steps involved in performing raster analytics include identifying vector and raster datasets and using ST_RegionStats().
- Earth Engine in BigQuery enables data-driven decision-making in climate, disaster response, agriculture, methane emissions monitoring, and custom use cases.
- Examples of use cases include wildfire risk assessment, sustainable sourcing, methane emissions analysis, and custom analyses using various datasets.
- A detailed example demonstrates how to combine wildfire risk data with weather forecasts using ST_RegionStats() and SQL queries.
- The combination of datasets allows for insights on relative wildfire exposure and risk assessments, aiding in decision-making and visualization.
- Earth Engine in BigQuery opens up new possibilities for geospatial analytics, and more enhancements are expected in the future.
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