- 1Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China (23039814r@connect.polyu.hk)
- 2Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China (chouab@connect.ust.hk)
- 3Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China (fanzhanggis@pku.edu.cn)
Urban dwellers today frequently document their daily experiences through digital photography, sharing these moments across popular social networking platforms. While these platforms host vast collections of images, the geographical data associated with many photos is often imprecise or missing entirely. Accurately determining the geographic coordinates of user-submitted photographs adds substantial value to these visual records, offering practical applications in city development planning, architectural studies, and public safety monitoring. Despite its potential benefits, the process of precise image geo-localization remains technically complex and challenging. This study presents an innovative approach to geo-localize crowd-sourced images in urban settings, addressing the limitations of traditional methods. By combining street-view panoramas and satellite imagery through a novel contrastive learning framework, we significantly improve localization accuracy. Using Hong Kong as a case study, we demonstrate substantial improvements over existing approaches, reducing median and average errors by 77.4% and 63.6%, respectively. Surprisingly, our findings reveal that satellite imagery alone outperforms street-view data in geo-localization tasks, challenging previous assumptions. This research not only advances the field of urban image geo-localization but also provides a valuable multi-source benchmark, paving the way for future innovations in urban sensing, mapping, and analysis across various disciplines.
How to cite: Hou, Q., Hou, C., Zhang, F., and Weng, Q.: Crowd-sourced images geo-localization method based on multi-modal deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14877, https://doi.org/10.5194/egusphere-egu25-14877, 2025.