University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

Zheng, Z; Wei, Y; Yang, Y

University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

Zheng, Z

Wei, Y Yang, Y

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: MM '20: Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 1395-1403
Issue Date:: 2020-02-28

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Field	Value	Language
dc.contributor.author	Zheng, Z https://orcid.org/0000-0002-2434-9050
dc.contributor.author	Wei, Y
dc.contributor.author	Yang, Y
dc.date	2020-10-12
dc.date.accessioned	2021-04-11T06:52:25Z
dc.date.available	2021-04-11T06:52:25Z
dc.date.issued	2020-02-28
dc.identifier.citation	MM '20: Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 1395-1403
dc.identifier.isbn	978-1-4503-7988-5
dc.identifier.uri	http://hdl.handle.net/10453/147990
dc.description.abstract	We consider the problem of cross-view geo-localization. The primary challenge is to learn the robust feature against large viewpoint changes. Existing benchmarks can help, but are limited in the number of viewpoints. Image pairs, containing two viewpoints, e.g., satellite and ground, are usually provided, which may compromise the feature learning. Besides phone cameras and satellites, in this paper, we argue that drones could serve as the third platform to deal with the geo-localization problem. In contrast to traditional ground-view images, drone-view images meet fewer obstacles, e.g., trees, and provide a comprehensive view when flying around the target place. To verify the effectiveness of the drone platform, we introduce a new multi-view multi-source benchmark for drone-based geo-localization, named University-1652. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. As the name implies, drone-view target localization intends to predict the location of the target place via drone-view images. On the other hand, given a satellite-view query image, drone navigation is to drive the drone to the area of interest in the query. We use this dataset to analyze a variety of off-the-shelf CNN features and propose a strong CNN baseline on this challenging dataset. The experiments show that University-1652 helps the model to learn viewpoint-invariant features and also has good generalization ability in real-world scenarios.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	MM '20: Proceedings of the 28th ACM International Conference on Multimedia
dc.relation.ispartof	ACM International Conference on Multimedia
dc.relation.isbasedon	10.1145/3394171.3413896
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
dc.type	Conference Proceeding
utslib.location.activity	Virtual
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-11T06:51:45Z
pubs.finish-date	2020-10-16
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-10-12
dc.location	USA

Abstract:

We consider the problem of cross-view geo-localization. The primary challenge is to learn the robust feature against large viewpoint changes. Existing benchmarks can help, but are limited in the number of viewpoints. Image pairs, containing two viewpoints, e.g., satellite and ground, are usually provided, which may compromise the feature learning. Besides phone cameras and satellites, in this paper, we argue that drones could serve as the third platform to deal with the geo-localization problem. In contrast to traditional ground-view images, drone-view images meet fewer obstacles, e.g., trees, and provide a comprehensive view when flying around the target place. To verify the effectiveness of the drone platform, we introduce a new multi-view multi-source benchmark for drone-based geo-localization, named University-1652. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. As the name implies, drone-view target localization intends to predict the location of the target place via drone-view images. On the other hand, given a satellite-view query image, drone navigation is to drive the drone to the area of interest in the query. We use this dataset to analyze a variety of off-the-shelf CNN features and propose a strong CNN baseline on this challenging dataset. The experiments show that University-1652 helps the model to learn viewpoint-invariant features and also has good generalization ability in real-world scenarios.

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http://hdl.handle.net/10453/147990