Geospatial analysis is predicated on the ability to evaluate geospatial coincidence between geo- referenced objects. The sheer volume of remotely sensed data and their irregular spacing are a disabling roadblock for scientists, currently only circumventable by spatiotemporal discretization and sampling of observations. While spatial discretization simplifies the evaluation of geospatial coincidence, it decreases the data fidelity. The alternative geospatial referencing and indexing schema, the Spatio-Temporal Adaptive-Resolution Encoding (STARE), built on top of a Hierarchical Triangular Mesh (HTM), allows performant spatial coincidence evaluation of undiscretized observation. We present the software collection built around STARE that en- ables scientists to process remote sensing data at the actual sensor geolocation accuracy and resolution. It contains methods to read conventional geospatial data and to convert legacy representation into STARE representation. It further contains STARE based geoprocessing methods and storage backends for STARE indexed data.

• https://github.com/SpatioTemporal/STARE
• https://github.com/SpatioTemporal/STAREPandas
• https://github.com/SpatioTemporal/pystare
• https://github.com/SpatioTemporal/STAREMaster_py
• https://github.com/SpatioTemporal/STAREPods_py
• https://github.com/NiklasPhabian/mod2htm