Anaximander: Interactively Running Geospatial Deep Learning Models on Any Compute Backend
Applying deep learning models to satellite imagery from within geographic information systems (GIS) remains high-friction for remote sensing practitioners. Models arrive in incompatible formats and target different compute environments, from local workstations to serverless cloud services. As a result, every evaluation demands custom deployment, tiling, and georeferencing code before a single prediction reaches the analyst's map. This friction discourages systematic comparison in a domain where model choice directly affects operational outcomes such as field delineation, crop monitoring, and disaster response. We present Anaximander, an open-source system that unifies model source and com...
| Authors | Satej S. Soman, Akram Zaytar, Girmaw A. Tadesse et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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