arXiv:2606. 13896v1 Announce Type: cross Abstract: Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize.
By Julia Romero, Qin Lv, Morteza Karimzadeh
arXiv:2609.37481v1 Announce Type: new
Abstract: Large-scale pervasive sensing increasingly relies on high-resolution satellite imagery, yet task-specific onboard vision is constrained by costly annot...
By Ahmed Abdelnaby, Mohamed Elmahallawy, Marius Bernahrndt, Tobias Hecking
STEAM is a self‑training framework for unsupervised cross‑view geo‑localization that eliminates the need for image‑generation warm‑ups or clustering. It introduces a Stable Spatial‑Aware Module for bounded nonlinear spatial weighting, Elastic Matching with Bidirectional Top‑K Soft Matching and Dynamic Threshold Filtering, and Adaptive Purification that updates pseudo‑labels based on confidence, age, and expiration. On the University‑1652 dataset, STEAM outperforms other unsupervised methods in both retrieval directions and shows competitive results on SUES‑200 and DenseUAV.
By Shaoxiang Wang, Kejia Zhang, Haiwei Pan, Lan Zhang, Guozheng Zheng
arXiv:2608.29426v1 Announce Type: cross
Abstract: Reliable semantic representations derived from city-scale 3D models are increasingly important for urban analysis, infrastructure monitoring, autonom...
By Alexander Rusnak, Sophia Kovalenko, Jingru Wang, Ismail Moudden, Xiru Wang, Fr\'ed\'eric Kaplan
arXiv:2504. 11171v5 Announce Type: replace-cross Abstract: We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO).
By Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Bernabe-Moreno, Nicolas Long\'ep\'e
Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task. Most geospatial benchmarks evaluate representations solely through downstream tasks, providing limited insight into the information encoded within the representation itself.