GeoSANE: Learning Geospatial Representations from Models, Not Data

TL;DR

GeoSANE is a novel framework that learns a unified neural representation from existing geospatial models, enabling on-demand weight generation for various tasks and outperforming traditional training methods across multiple datasets.

Contribution

It introduces GeoSANE, a model foundry that generates neural network weights from existing foundation models, unifying geospatial knowledge for diverse tasks without retraining from scratch.

Findings

Generated models outperform models trained from scratch.

Matches or surpasses state-of-the-art remote sensing models.

Effective across ten diverse datasets and multiple modalities.

Abstract

Recent advances in remote sensing have led to an increase in the number of available foundation models; each trained on different modalities, datasets, and objectives, yet capturing only part of the vast geospatial knowledge landscape. While these models show strong results within their respective domains, their capabilities remain complementary rather than unified. Therefore, instead of choosing one model over another, we aim to combine their strengths into a single shared representation. We introduce GeoSANE, a geospatial model foundry that learns a unified neural representation from the weights of existing foundation models and task-specific models, able to generate novel neural networks weights on-demand. Given a target architecture, GeoSANE generates weights ready for finetuning for classification, segmentation, and detection tasks across multiple modalities. Models generated by GeoSANE consistently outperform their counterparts trained from scratch, match or surpass state-of-the-art remote sensing foundation models, and outperform models obtained through pruning or knowledge distillation when generating lightweight networks. Evaluations across ten diverse datasets and on GEO-Bench confirm its strong generalization capabilities. By shifting from pre-training to weight generation, GeoSANE introduces a new framework for unifying and transferring geospatial knowledge across models and tasks. Code is available at \href{https://hsg-aiml.github.io/GeoSANE/}{hsg-aiml.github.io/GeoSANE/}.