A Survey of the Research Progress in Image Geo-localization

被引：0
|
作者：
Huang G. [1 ]
Zhou Y. [1 ]
Hu X. [1 ]
Zhao L. [1 ]
Zhang C. [1 ]
机构：
[1] Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou
来源：
Journal of Geo-Information Science | 2023年 / 25卷 / 07期
关键词：
cyberspace surveying and mapping; datasets; evaluation method; image geo- localization; image retrieval; multi- sources data;
D O I：
10.12082/dqxxkx.2023.230073
中图分类号：
学科分类号：
摘要：
Image geo-localization is a technique that obtains the geographic location information of an image through a series of methods, so as to establish a mapping relationship with the real geographic space. This technique is important for further image information mining and has potential application value in cyberspace surveying and mapping, intelligence acquisition, user outdoor positioning, and augmented reality. Despite the tremendous progress in the field of computer vision, high-precision automatic geo-localization of images still needs to be addressed due to the involvement of multiple fields such as image feature extraction, large-scale data retrieval, large-scale point cloud processing, deep learning, geographic information feature extraction, geometric modeling and reasoning, semantic scene understanding, context-based reasoning, and multiple data fusion. This paper reviews the progress of image geo-localization research, mainly including image geo-localization methods, image geo-localization datasets, image geo-localization evaluation methods, and summary and prospect of image geo-localization. Firstly, image geolocation methods are classified into three categories, i.e., image retrieval, 2D-3D matching, and cross-modal retrieval, according to the relevance of the research content. Secondly, the datasets and evaluation methods used for image geo-localization research are categorized and summarized. The geolocalization datasets include image datasets, cross-view datasets, Structure from Motion (SfM) datasets, and multimodal datasets, etc. The image geo-localization evaluation metrics include Top-k candidates, localization error, position and orientation error per video frame, and accuracy/recall. Finally, the current status of image geo-localization research is analyzed, and the future research directions of image geo-localization are outlined in terms of global geo-localization, natural area geo-localization, multi-method fusion for geo-localization, Point of Interest (POI) data-based geo-localization, and pre-selected location refinement. © 2023 Journal of Geo-Information Science. All rights reserved.
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页码：1336 / 1362
页数：26
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