We’re excited to announce a brand new characteristic that simplifies exporting tabular knowledge from Earth Engine into BigQuery. Earth Engine and BigQuery share the aim of constructing large-scale knowledge processing accessible and usable by a wider vary of individuals and purposes; Earth Engine tends to concentrate on picture (raster) processing, whereas BigQuery is optimized for processing giant tabular datasets. This new connection is our first main step in direction of a deeper interoperability between the 2 platforms.
For years, customers have moved Earth Engine knowledge into BigQuery, however, till now, that required cautious consideration to encodings, intermediate storage, and knowledge varieties. At this time, we are able to supply extra juice for much less squeeze, with a single-line invocation to switch Earth Engine knowledge into BigQuery. This new `Export.desk.toBigQuery()` operate makes quite a lot of new flows less complicated, together with:
combining Earth Engine knowledge with BigQuery knowledge sources to get a extra full image of a selected downside
utilizing BigQuery’s highly effective evaluation instruments to extract insights from Earth Engine knowledge
sharing Earth Engine knowledge with SQL-friendly customers in a means that is accessible for them
This information walks by the method of exporting knowledge from Earth Engine to BigQuery, constructing a real-world instance of utilizing Google’s geospatial instruments to establish flooded roads.
Instance: Flooded street detection
Excessive climate occasions have a devastating affect all over the world. Flooding, warmth waves, and drought have substantial human and monetary prices, inflicting mortality and devastation of properties and property. The next instance exhibits tips on how to use satellite tv for pc knowledge mosaics from Earth Engine and open street datasets from BigQuery, processing the information in each environments to find out which street segments are affected by a flooding occasion within the UK.