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

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

Figures (8)

• Figure 1: rs-embed architecture overview.
• Figure 2: Parallel batch export pipeline (orchestration + I/O prefetch + inference + async export) that decouples network, compute, and disk writes to sustain high throughput reliably.
• Figure 3: Performance comparison of different rsfm embeddings for maize yield regression in Illinois. The top row shows bar plots of test-set $R^2$ and RMSE (mt/ha) for each model. The bottom row presents predicted (Pred) vs. true (True) scatter plots for the top three models, with the dashed line indicating the ideal 1:1 reference.
• Figure 4: Spatial maps of test predictions and residuals in Illinois(Argrifm). Left: predicted maize yield (mt/ha). Right: residuals (pred - true), where red indicates overestimation and blue indicates underestimation.
• Figure 5: Visualization of embeddings from different models (OutputSpec.grid(), input_prep='resize'). The numbers in parentheses denote the channel, height, and width. For visualization, we apply PCA and use the top three principal components as pseudo-RGB channels.