# Multi-Spectral Remote Sensing Image Retrieval Using Geospatial Foundation Models

arXiv: https://arxiv.org/abs/2403.02059 · HTML: https://mlsanity.com/paper/2403.02059/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)
- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery](https://mlsanity.com/paper/2011.05479/) (2020)
- [Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images](https://mlsanity.com/paper/1904.01258/) (2019)
- [A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing](https://mlsanity.com/paper/2202.11429/) (2022)
- [Transductive Zero-Shot Hashing for Multilabel Image Retrieval](https://mlsanity.com/paper/1911.07192/) (2019)

## Built on by (most cited first, 6 of 6)

- [Lossy Neural Compression for Geospatial Analytics: A Review](https://mlsanity.com/paper/2503.01505/) (2025)
- [FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensing](https://mlsanity.com/paper/2509.23927/) (2025)
- [Earth Embeddings Reveal Diverse Urban Signals from Space](https://mlsanity.com/paper/2604.03456/) (2026)
- [FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery](https://mlsanity.com/paper/2602.19190/) (2026)
- [A Resource-Efficient Training Framework for Remote Sensing Text--Image Retrieval](https://mlsanity.com/paper/2501.10638/) (2025)
- [The View From Space: Navigating Instrumentation Differences with EOFMs](https://mlsanity.com/paper/2510.03316/) (2025)

## Abstract (arXiv)

Image retrieval enables an efficient search through vast amounts of satellite imagery and returns similar images to a query. Deep learning models can identify images across various semantic concepts without the need for annotations. This work proposes to use Geospatial Foundation Models, like Prithvi, for remote sensing image retrieval with multiple benefits: i) the models encode multi-spectral satellite data and ii) generalize without further fine-tuning. We introduce two datasets to the retrieval task and observe a strong performance: Prithvi processes six bands and achieves a mean Average Precision of 97.62% on BigEarthNet-43 and 44.51% on ForestNet-12, outperforming other RGB-based models. Further, we evaluate three compression methods with binarized embeddings balancing retrieval speed and accuracy. They match the retrieval speed of much shorter hash codes while maintaining the same accuracy as floating-point embeddings but with a 32-fold compression. The code is available at https://github.com/IBM/remote-sensing-image-retrieval.