{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:59:02Z","timestamp":1767085142911},"reference-count":6,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.14778\/3554821.3554865","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3646-3649","source":"Crossref","is-referenced-by-count":7,"title":["Satellite image search in AgoraEO"],"prefix":"10.14778","volume":"15","author":[{"given":"Ahmet Kerem","family":"Aksoy","sequence":"first","affiliation":[{"name":"TU Berlin"}]},{"given":"Pavel","family":"Dushev","sequence":"additional","affiliation":[{"name":"SAP Labs"}]},{"given":"Eleni Tzirita","family":"Zacharatou","sequence":"additional","affiliation":[{"name":"IT University of Copenhagen"}]},{"given":"Holmer","family":"Hemsen","sequence":"additional","affiliation":[{"name":"DFKI"}]},{"given":"Marcela","family":"Charfuelan","sequence":"additional","affiliation":[{"name":"DFKI"}]},{"given":"Jorge-Arnulfo","family":"Quian\u00e9-Ruiz","sequence":"additional","affiliation":[{"name":"TU Berlin and DFKI"}]},{"given":"Beg\u00fcm","family":"Demir","sequence":"additional","affiliation":[{"name":"TU Berlin"}]},{"given":"Volker","family":"Markl","sequence":"additional","affiliation":[{"name":"TU Berlin and DFKI"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. DIAS Platforms. Retrieved March 15 2022 from https:\/\/www.copernicus.eu\/en\/access-data\/dias  [n.d.]. DIAS Platforms. Retrieved March 15 2022 from https:\/\/www.copernicus.eu\/en\/access-data\/dias"},{"key":"e_1_2_1_2_1","volume-title":"Proc. Big Data from Space (BiDS).","author":"de Wall Arne","year":"2021","unstructured":"Arne de Wall , Bj\u00f6rn Deiseroth , Eleni Tzirita Zacharatou , Jorge-Arnulfo Quian\u00e9-Ruiz , Beg\u00fcm Demir , and Volker Markl . 2021 . Agora-EO: A Unified Ecosystem for Earth Observation - A Vision for Boosting EO Data Literacy - . In Proc. Big Data from Space (BiDS). Arne de Wall, Bj\u00f6rn Deiseroth, Eleni Tzirita Zacharatou, Jorge-Arnulfo Quian\u00e9-Ruiz, Beg\u00fcm Demir, and Volker Markl. 2021. Agora-EO: A Unified Ecosystem for Earth Observation - A Vision for Boosting EO Data Literacy -. In Proc. Big Data from Space (BiDS)."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.2974629"},{"key":"e_1_2_1_4_1","first-page":"174","article-title":"BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval","volume":"9","author":"Sumbul Gencer","year":"2021","unstructured":"Gencer Sumbul , Arne de Wall , Tristan Kreuziger , Filipe Marcelino , Hugo Costa , Pedro Benevides , M\u00e1rio Caetane , Beg\u00fcm Demir , and Volker Markl . 2021 . BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval . IEEE GRSS Magazine 9 , 3 (2021), 174 -- 180 . Gencer Sumbul, Arne de Wall, Tristan Kreuziger, Filipe Marcelino, Hugo Costa, Pedro Benevides, M\u00e1rio Caetane, Beg\u00fcm Demir, and Volker Markl. 2021. BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval. IEEE GRSS Magazine 9, 3 (2021), 174--180.","journal-title":"IEEE GRSS Magazine"},{"key":"e_1_2_1_5_1","first-page":"150","article-title":"Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives. In Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. John Wiley & Sons, Hoboken, NJ, USA","volume":"11","author":"Sumbul G.","year":"2021","unstructured":"G. Sumbul , J. Kang , and B. Demir . 2021 . Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives. In Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. John Wiley & Sons, Hoboken, NJ, USA , Chapter 11 , 150 -- 160 . G. Sumbul, J. Kang, and B. Demir. 2021. Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives. In Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. John Wiley & Sons, Hoboken, NJ, USA, Chapter 11, 150--160.","journal-title":"Chapter"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3456859.3456861"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554865","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:32:49Z","timestamp":1672227169000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":6,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554865"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554865","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}