# Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives

arXiv: https://arxiv.org/abs/2004.01613 · HTML: https://mlsanity.com/paper/2004.01613/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing](https://mlsanity.com/paper/2401.07782/) (2024)
- [Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing](https://mlsanity.com/paper/2201.08125/) (2022)
- [A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing](https://mlsanity.com/paper/2202.11429/) (2022)
- [Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval](https://mlsanity.com/paper/2105.03647/) (2021)
- [Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing](https://mlsanity.com/paper/2204.08707/) (2022)
- [REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrieval](https://mlsanity.com/paper/2504.03169/) (2025)
- [Satellite Image Search in AgoraEO](https://mlsanity.com/paper/2208.10830/) (2022)
- [A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval](https://mlsanity.com/paper/2201.06459/) (2022)
- [Annotation Cost Efficient Active Learning for Content Based Image Retrieval](https://mlsanity.com/paper/2306.11605/) (2023)
- [Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval](https://mlsanity.com/paper/2406.10107/) (2024)

## Abstract (arXiv)

This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. Initially, we analyze the limitations of the traditional CBIR systems that rely on the hand-crafted RS image descriptors. Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. In particular, we present the theoretical properties of the most recent DL based CBIR systems for the characterization of the complex semantic content of RS images. After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. Finally, the most promising research directions in RS CBIR are discussed.