[2602.23678] Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.23678 (cs) [Submitted on 27 Feb 2026] Title:Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand Authors:Dingqi Ye, Daniel Kiv, Wei Hu, Jimeng Shi, Shaowen Wang View a PDF of the paper titled Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand, by Dingqi Ye and 4 other authors View PDF HTML (experimental) Abstract:The remote sensing community is witnessing a rapid growth of foundation models, which provide powerful embeddings for a wide range of downstream tasks. However, practical adoption and fair comparison remain challenging due to substantial heterogeneity in model release formats, platforms and interfaces, and input data specifications. These inconsistencies significantly increase the cost of obtaining, using, and benchmarking embeddings across models. To address this issue, we propose rs-embed, a Python library that offers a unified, region of interst (ROI) centric interface: with a single line of code, users can retrieve embeddings from any supported model for any location and any time range. The library also provides efficient batch processing to enable large-scale embedding generation and evaluation. The code is available at: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) ACM classes: I.4.9; I.2.6; H.2.8; D.2.12 Cite as: arXiv:2602.23678 [cs.CV]   (or arXiv:2602.23678v1 [...