Towards Global, General-Purpose Pretrained Geographic Location Encoders (Papers Track)

Konstantin Klemmer (Microsoft Research); ESTHER ROLF (Google Research); Caleb Robinson (Microsoft AI for Good Research Lab); Lester Mackey (Microsoft Research); Marc Rußwurm (École Polytechnique Fédérale de Lausanne)

Paper PDF Poster File NeurIPS 2023 Poster Cite
Unsupervised & Semi-Supervised Learning Computer Vision & Remote Sensing


Location information is essential for modeling tasks in climate-related fields ranging from ecology to the Earth system sciences. However, obtaining meaningful location representation is challenging and requires a model to distill semantic location information from available data, such as remote sensing imagery. To address this challenge, we introduce SatCLIP, a global, general-purpose geographic location encoder that provides vector embeddings summarizing the characteristics of a given location for convenient usage in diverse downstream tasks. We show that SatCLIP embeddings, pretrained on multi-spectral Sentinel-2 satellite data, can be used for various predictive out-of-domain tasks, including temperature prediction and animal recognition in imagery, and outperform existing competing approaches. SatCLIP embeddings also prove helpful in overcoming geographic domain shift. This demonstrates the potential of general-purpose location encoders and opens the door to learning meaningful representations of our planet from the vast, varied, and largely untapped modalities of geospatial data.