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 compute location choice
behind one interactive interface. The system’s backend is an inference server that loads models from
multiple commonly used sources and serves them on any accessible compute backend. The server provides
session management and model caching, and streams results back per tile. The backend is paired with a
QGIS plugin that drives tiling, result reassembly, georeferencing, and real-time per-tile status visualization.
An additional user-interface path injects layer legends as prompts into vision-language models. We
demonstrate the system in a code-free side-by-side comparison of three heterogeneous models on an
agricultural field delineation task: gpt-image-1 via a cloud API, Segment Anything Model 3 (SAM 3)
on a remote GPU, and DelineateAnything on a local CPU. The inference backend and protocol are
open-source and available at https://github.com/microsoft/nxmndr (opens in new tab).