# Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand

arXiv: https://arxiv.org/abs/2602.23678 · HTML: https://mlsanity.com/paper/2602.23678/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data](https://mlsanity.com/paper/2507.22291/) (2025)
- [TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis](https://mlsanity.com/paper/2506.20380/) (2025)
- [FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring](https://mlsanity.com/paper/2312.10114/) (2023)
- [TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation](https://mlsanity.com/paper/2506.06281/) (2025)
- [WildSAT: Learning Satellite Image Representations from Wildlife Observations](https://mlsanity.com/paper/2412.14428/) (2024)
- [THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications](https://mlsanity.com/paper/2601.16011/) (2026)

## Built on by (most cited first, 4 of 4)

- [UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation](https://mlsanity.com/paper/2606.23503/) (2026)
- [Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping](https://mlsanity.com/paper/2608.12663/) (2026)
- [Earth Embeddings](https://mlsanity.com/paper/2608.03410/) (2026)
- [Applying foundation model embeddings towards urban livability evaluation](https://mlsanity.com/paper/2609.09429/) (2026)

## Abstract (arXiv)

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