> the state of the art of machine learning which uses geospatial data (e.g. GeoJSON) as both input and output > There is no such state of the art Some GIS wor

> the state of the art of machine learning which uses geospatial data (e.g. GeoJSON) as both input and output > There is no such state of the art Some GIS work uses vector data: points/lines/polygons representing features (e.g., the location of roads or the outlines of buildings), which can be stored in formats like GeoJSON or WKT. But other work uses remote sensing data/satellite imagery that can be stored in raster formats like GeoTIFF - essentially TIFF image files with additional information stored to georeference them. You can totally do machine learning on satellite imagery where both the input and output are geospatial data (e.g. to categorise land use - the inputs are multispectral images and the outputs can be images where the value of each pixel represents the identified land use). You can also use machine learning for tasks like building footprint detection/delineation (e.g., [1]) based on satellite imagery. The output from such a pipeline can be a set of polygons, which could be saved as GeoJSON. I'd consider either of theses to be examples of "machine learning which uses geospatial data (e.g. GeoJSON) as both input and output". [1]: https://azure.microsoft.com/en-us/blog/how-to-extract-building-footprints-from-satellite-images-using-deep-learning/ https://azure.microsoft.com/en-us/blog/how-to-extract-buildi...