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

2026-02-27

AI Summary

This work addresses the high cost of embedding acquisition, usage, and fair comparison among remote sensing foundation models, which stems from their highly heterogeneous release formats, interfaces, and input specifications. To this end, we propose rs-embed, a Python library centered on regions of interest (ROIs) that, for the first time, offers a unified interface to abstract away model heterogeneity. It enables on-demand retrieval of embeddings from any integrated model across arbitrary spatiotemporal extents via a single line of code. The ROI-driven architecture, combined with an efficient batching mechanism, substantially lowers the barrier to entry and enhances the efficiency of large-scale embedding generation, deployment, and standardized evaluation, thereby promoting reproducibility and broader adoption of remote sensing foundation models.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Other Foundations of Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search
• Web Mining and Content Analysis: Large pretrained models with web data

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: https://github.com/cybergis/rs-embed

Problem

• foundation models
• remote sensing
• model heterogeneity
• embedding retrieval
• benchmarking

Innovation

• foundation model
• remote sensing
• embedding
• unified interface
• ROI-centric