# Satellite Image Search in AgoraEO

arXiv: https://arxiv.org/abs/2208.10830 · HTML: https://mlsanity.com/paper/2208.10830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images](https://mlsanity.com/paper/1904.01258/) (2019)
- [Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives](https://mlsanity.com/paper/2004.01613/) (2020)

## Built on by (most cited first, 1 of 1)

- [Reaching the Edge of the Edge: Image Analysis in Space](https://mlsanity.com/paper/2301.04954/) (2023)

## Abstract (arXiv)

The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives. MiLaN is a deep hashing network based on metric learning that encodes high-dimensional image features into compact binary hash codes. We use these codes as keys in a hash table to enable real-time nearest neighbor search and highly accurate retrieval. In this demonstration, we showcase the efficiency of MiLaN by integrating it with EarthQube, a browser and search engine within AgoraEO. EarthQube supports interactive visual exploration and Query-by-Example over satellite image repositories. Demo visitors will interact with EarthQube playing the role of different users that search images in a large-scale remote sensing archive by their semantic content and apply other filters.