  Geographic information is essential for modeling tasks in fields ranging from
ecology to epidemiology. However, extracting relevant location characteristics
for a given task can be challenging, often requiring expensive data fusion or
distillation from massive global imagery datasets. To address this challenge,
we introduce Satellite Contrastive Location-Image Pretraining (SatCLIP). This
global, general-purpose geographic location encoder learns an implicit
representation of locations by matching CNN and ViT inferred visual patterns of
openly available satellite imagery with their geographic coordinates. The
resulting SatCLIP location encoder efficiently summarizes the characteristics
of any given location for convenient use in downstream tasks. In our
experiments, we use SatCLIP embeddings to improve prediction performance on
nine diverse location-dependent tasks including temperature prediction, animal
recognition, and population density estimation. Across tasks, SatCLIP
consistently outperforms alternative location encoders and improves geographic
generalization by encoding visual similarities of spatially distant
environments. These results demonstrate the potential of vision-location models
to learn meaningful representations of our planet from the vast, varied, and
largely untapped modalities of geospatial data.
