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Satellite Image Search in AgoraEO

Ahmet Kerem Aksoy; Pavel Dushev; Eleni Tzirita Zacharatou; Holmer Hemsen; Marcela Charfuelan Oliva; Jorge Arnulfo Quiane Ruiz; Begüm Demir; Volker Markl
In: Fatma Ozcan; Juliana Freire; Xuemin Li (Hrsg.). Proceedings of the 48th International Conference on Very Large Databases. International Conference on Very Large Data Bases (VLDB-2022), September 5-9, Sydney, Australia, Pages 3646-3649, Vol. 15, No. 12, VLDB Endowment Inc, 8/2022.


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 metric-learning-based deep hashing network that encodes high-dimensional image features into compact binary hash codes. We then 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.

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