Content-Aware Hierarchical Representation Selection for Cross-View Geo-Localization

Lu, Zixing; Pu, Tao; Chen, Tianshui; Li, Lin

doi:10.1007/978-3-031-26348-4_16

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…As a fundamental component of studying image-based geo-localization, datasets play a critical role in exploring the challenges faced in real-world applications. To construct datasets, researchers collect abundant images to create query images and a database, which is mainly captured by three views from distinct altitudes: ground [15][16][17], unmanned aerial vehicle (UAV) [18,19], and satellite [20]. Each view corresponds to specific applications of geo-localization, such as ground for mobile robots, UAV for drone navigation, and satellite for Earth observation.…”

Section: Introductionmentioning

confidence: 99%

MTGL40-5: A Multi-Temporal Dataset for Remote Sensing Image Geo-Localization

Ma,

Pei,

Yang

et al. 2023

Remote Sensing

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Image-based geo-localization focuses on predicting the geographic information of query images by matching them with annotated images in a database. To facilitate relevant studies, researchers collect numerous images to build the datasets, which explore many challenges faced in real-world geo-localization applications, significantly improving their practicability. However, a crucial challenge that often arises is overlooked, named the cross-time challenge in this paper, i.e., if query and database images are taken from the same landmark but at different time periods, the significant difference in their image content caused by the time gap will notably increase the difficulty of image matching, consequently reducing geo-localization accuracy. The cross-time challenge has a greater negative influence on non-real-time geo-localization applications, particularly those involving a long time span between query and database images, such as satellite-view geo-localization. Furthermore, the rough geographic information (e.g., names) instead of precise coordinates provided by most existing datasets limits the geo-localization accuracy. Therefore, to solve these problems, we propose a dataset, MTGL40-5, which contains remote sensing (RS) satellite images captured from 40 large-scale geographic locations spanning five different years. These large-scale images are split to create query images and a database with landmark labels for geo-localization. By observing images from the same landmark but at different time periods, the cross-time challenge becomes more evident. Thus, MTGL40-5 supports researchers in tackling this challenge and further improving the practicability of geo-localization. Moreover, it provides additional geographic coordinate information, enabling the study of high-accuracy geo-localization. Based on the proposed MTGL40-5 dataset, many existing geo-localization methods, including state-of-the-art approaches, struggle to produce satisfactory results when facing the cross-time challenge. This highlights the importance of proposing MTGL40-5 to address the limitations of current methods in effectively solving the cross-time challenge.

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