Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915821620559872 |
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| author | Ye, Dingqi Kiv, Daniel Hu, Wei Shi, Jimeng Wang, Shaowen |
| author_facet | Ye, Dingqi Kiv, Daniel Hu, Wei Shi, Jimeng Wang, Shaowen |
| contents | 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: https://github.com/cybergis/rs-embed |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23678 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand Ye, Dingqi Kiv, Daniel Hu, Wei Shi, Jimeng Wang, Shaowen Computer Vision and Pattern Recognition Machine Learning I.4.9; I.2.6; H.2.8; D.2.12 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: https://github.com/cybergis/rs-embed |
| title | Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.9; I.2.6; H.2.8; D.2.12 |
| url | https://arxiv.org/abs/2602.23678 |