Author(s)

Lin Zhao^1,2, Yanru Liu^1*, Yuefeng Lu^1,3,4*, Ying Sun¹, Jing Li¹, Kaizhong Yao¹

¹School of Civil and Architectural Engineering, Shandong University of Technology, Zibo, China.
²School of Geography Science, Nanjing Normal University, Nanjing, China.
³Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan, China.
⁴State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.

Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.

Deep Learning, Vector Matching, Similarity, VGG16, Siamese Network

Zhao, L. , Liu, Y. , Lu, Y. , Sun, Y. , Li, J. and Yao, K. (2023) Research on Vector Road Data Matching Method Based on Deep Learning. Journal of Applied Mathematics and Physics, 11, 303-315. doi: 10.4236/jamp.2023.111017.