文献类型：期刊文献

英文题名：A Transformer-based Multi-modal Model for Urban-Rural Fringe Identification

作者：Jia, Furong[1]; Dong, Quanhua[1]; Huang, Zhou[1]; Chen, Xiao-Jian[1]; Wang, Yi[1]; Peng, Xia[2]; Guo, Yuan[3]; Ma, Ruixian[1]; Zhang, Fan[1]; Liu, Yu[1]

第一作者：Jia, Furong

机构：[1] Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China; [2] Tourism College, Beijing Union University, Beijing, China; [3] School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, China

第一机构：Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

年份：2024

起止页码：1-10

外文期刊名：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

收录：EI(收录号：20243316865238);Scopus(收录号：2-s2.0-85200799346)

语种：英文

外文关键词：Data visualization - Deep learning - Image processing - Rural areas

摘要：As the frontier of urbanization, urban-rural fringes (URFs) transitionally connect urban construction regions to the rural hinterland, and its identification is significant for the study of urbanization-related socioeconomic changes and human dynamics. Previous research on URF identification has predominantly relied on remote sensing data, which often provides a uniform overhead perspective with limited spatial resolution. As an additional data source, Street View Images (SVIs) offer a valuable human-related perspective, efficiently capturing intricate transitions from urban to rural areas. However, the abundant visual information offered by SVIs has often been overlooked and multi-modal techniques have seldom been explored to integrate multi-source data for delineating URFs. To address this gap, this study proposes a Transformed-based multi-modal methodology for identifying URFs, which includes a street view panorama classifier and a remote sensing classification model. In the study area of Beijing, the experimental results indicate that an URF with a total area of 731.24 km2 surrounds urban cores, primarily located between the fourth and sixth ring roads. The effectiveness of the proposed method is demonstrated through comparative experiments with traditional URF identification methods. Additionally, a series of ablation studies demonstrate the efficacy of incorporating multi-source data. Based on the delineated URFs in Beijing, this research introduced Points of Interest (POI) data and commuting data to analyze the socioeconomic characteristics of URFs. The findings indicate that URFs are characterized by longer commuting distances and less diverse restaurant consumption patterns compared to more urbanized regions. This study enables the accurate identification of URFs through the Transform-based multi-modal approach integrating street view images. Furthermore, it provides a humancentric comprehension of URFs, which is essential for informing strategies of urban planning and development. Authors