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 -