Data management and interoperability is a challenge when solving geospatial problems with machine learning. Existing ML tools and standards outside of GIS are inadequate when dealing with the idiosyncrasies of geospatial data. Additionally, data acquisition and labeling are expensive processes. Human-in-the-loop workflows can help machine learning models improve more quickly.

In this workshop attendees will learn how to use data compliant with the STAC specification to organize and create training data, models, and predictions to power applications. Workshop attendees will learn what STAC is and how to use tools like PySTAC, Raster Vision, Franklin, and GroundWork to create a human-in-the-loop AI pipeline from beginning to end.