Bayer Crop Science recently unified internal imagery platform capabilities by completing a full re-write of core imagery APIs to leverage the performance gains offered by Cloud Optimized GeoTIFFs (COGs) with the efficiencies and extensibility of the SpatioTemporal Asset Catalog (STAC). By standardizing imagery pipeline outputs on COGs, all developers and imagery scientists at Bayer Crop Science have access to the full spatial imagery catalog as STAC Item/Asset records and can implement common file access patterns. One potential benefit of adopting STAC-accessible COGs as a standard pipeline output is the ability to squeeze out unnecessary data transfers for local writes and reads of unwanted peripheral pixels at-scale for imagery-based ML training and processing. To do this, our imagery team developed a new pilot Imagery-as-Array API to return band-specific AOI targeted range-and-column pixels as numPy arrays for processing and analysis. By implementing data transfers, reads, and writes for only targeted pixels, the resulting milliseconds saved here and there for 1000’s of images can add up to hours of unrealized network and compute time in very short order and lead to faster iterations of higher quality. The overall re-write effort aligned with Bayer global digital transformation objectives and firmly established the imagery platform as a scalable and durable pivot between imagery capture, post-processing, and decision-science based analytics to help drive future research and commercial advancements. This presentation will provide an overview of the Bayer Crop Science imagery ecosystem and the incremental efficiencies gained from integrating COGs with other open source software capabilities.

Bayer Crop Science has engaged in a multi-year collaboration with Sparkgeo Consulting to deliver an evolving set of spatial imagery search, discovery, and visualization capabilities built on top of open source geospatial software. Initial solutions integrated CKAN, Geoserver, and Geotrellis to pre-render custom tilesets for derived analytic outputs. This process proved difficult to scale with increasing ingest rates and led to standardizing imagery pipeline outputs on Cloud Optimized GeoTIFFs(COGs) with rio-tiler, pyproj, GDAL , Shapely, and Rasterio for processing to define dynamic rendering visualization products in a newly developed STAC-compliant catalog. The Sparkgeo team has written a custom Global Imagery Search tool for our corporate OpenLayers-enabled application framework which combines event-based per-scene visualization processing with STAC search results and TMS-to-COG range/column searches. The Global Imagery Search tool also allows client/application side dynamic color map rendering. This presentation will describe the evolution from tilesets to dynamic rendered tiles and the customizations within STAC-collections needed to achieve this.