End-to-end unsupervised road network extraction based on drone images

Ming Zhou; Li Ma; Shengwei Lu; Tongyan Zhang; Qiang Wu; Xiaohong Liao; Sirui Shu; Shuangjiang Li

doi:10.1117/12.3029650

21 June 2024 End-to-end unsupervised road network extraction based on drone images

Ming Zhou, Li Ma, Shengwei Lu, Tongyan Zhang, Qiang Wu, Xiaohong Liao, Sirui Shu, Shuangjiang Li

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131672D (2024) https://doi.org/10.1117/12.3029650
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

The road network is an important reference standard in infrastructure planning. Whether the road network is complete or not better reflects the economic and humanistic conditions of urban construction. In this study, the current severe challenges in drone remote sensing road network extraction are addressed, such as low extraction efficiency and weak generalization performance. An end-to-end unsupervised road network extraction framework based on drone images is proposed. In comparison with the results of the best-supervised learning methods and unsupervised learning methods, the overall performance shows the best performance. Among them, the IOU and F1 scores reached 0.95 and 0.96, respectively. The method proposed in this study provides a new reference for feature extraction from drone images and can help with the real-time update of the road network.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Zhou, Li Ma, Shengwei Lu, Tongyan Zhang, Qiang Wu, Xiaohong Liao, Sirui Shu, and Shuangjiang Li "End-to-end unsupervised road network extraction based on drone images", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131672D (21 June 2024); https://doi.org/10.1117/12.3029650

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