RapidEarth: A Search-by-Classification Engine for Large-Scale Geospatial Imagery
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RapidEarth : A Search-by-Classification Engine for Large-Scale Geospatial Imagery. / Lülf, Christian; Martins, Denis Mayr Lima; Salles, Marcos Antonio Vaz; Zhou, Yongluan; Gieseke, Fabian.
31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023. red. / Maria Luisa Damiani; Matthias Renz; Ahmed Eldawy; Peer Kroger; Mario A. Nascimento. Association for Computing Machinery, Inc., 2023. s. 1-4 58 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - RapidEarth
T2 - 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
AU - Lülf, Christian
AU - Martins, Denis Mayr Lima
AU - Salles, Marcos Antonio Vaz
AU - Zhou, Yongluan
AU - Gieseke, Fabian
N1 - Funding Information: Fabian Gieseke acknowledges support from the Independent Research Fund Denmark (grant number 9131-00110B) and from the German Federal Ministry of Education and Research (AI4Forest project; grant number 01IS23025). Publisher Copyright: © 2023 ACM.
PY - 2023
Y1 - 2023
N2 - Data exploration and analysis in various domains often necessitate the search for specific objects in massive databases. A common search strategy, often known as search-by-classification, resorts to training machine learning models on small sets of positive and negative samples and to performing inference on the entire database to discover additional objects of interest. While such an approach often yields very good results in terms of classification performance, the entire database usually needs to be scanned, a process that can easily take several hours even for medium-sized data catalogs. In this work, we present RapidEarth, a geospatial search-by-classification engine that allows analysts to rapidly search for interesting objects in very large data collections of satellite imagery in a matter of seconds, without the need to scan the entire data catalog. RapidEarth embodies a co-design of multidimensional indexing structures and decision branches, a recently proposed variant of classical decision trees. These decision branches allow RapidEarth to transform the inference phase into a set of range queries, which can be efficiently processed by leveraging the aforementioned multidimensional indexing structures. The main contribution of this work is a geospatial search engine that implements these technical findings.
AB - Data exploration and analysis in various domains often necessitate the search for specific objects in massive databases. A common search strategy, often known as search-by-classification, resorts to training machine learning models on small sets of positive and negative samples and to performing inference on the entire database to discover additional objects of interest. While such an approach often yields very good results in terms of classification performance, the entire database usually needs to be scanned, a process that can easily take several hours even for medium-sized data catalogs. In this work, we present RapidEarth, a geospatial search-by-classification engine that allows analysts to rapidly search for interesting objects in very large data collections of satellite imagery in a matter of seconds, without the need to scan the entire data catalog. RapidEarth embodies a co-design of multidimensional indexing structures and decision branches, a recently proposed variant of classical decision trees. These decision branches allow RapidEarth to transform the inference phase into a set of range queries, which can be efficiently processed by leveraging the aforementioned multidimensional indexing structures. The main contribution of this work is a geospatial search engine that implements these technical findings.
KW - classification
KW - decision trees
KW - index structures
KW - search engine
U2 - 10.1145/3589132.3625601
DO - 10.1145/3589132.3625601
M3 - Article in proceedings
AN - SCOPUS:85182515213
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 1
EP - 4
BT - 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
A2 - Damiani, Maria Luisa
A2 - Renz, Matthias
A2 - Eldawy, Ahmed
A2 - Kroger, Peer
A2 - Nascimento, Mario A.
PB - Association for Computing Machinery, Inc.
Y2 - 13 November 2023 through 16 November 2023
ER -
ID: 381260233