08/2026 BIFOLD Colloquium

GeoSANE: Learning Geospatial Representations from Models, Not Data

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 network 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

BIO: Prof. Dr. Damian Borth (born 1981, in Opole, Poland) is a German computer scientist specializing in artificial intelligence and machine learning. He earned his Master’s degree in Informatics from the Technical University of Kaiserslautern in 2010 and completed his Ph.D. there in 2014 with a dissertation on socio‑video semantics.

In 2012, he was a Visiting Scholar at Columbia University’s Digital Video and Multimedia Lab in New York City. After his Ph.D., he conducted postdoctoral research at UC Berkeley and the International Computer Science Institute with Jitendra Malik and Alexei A. Efros. He later became Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI). Since 2018, he has been Full Professor of Artificial Intelligence and Machine Learning at the University of St. Gallen, Switzerland. He also serves as Academic Director of the university’s Ph.D. program in Computer Science.