文献类型：期刊文献

英文题名：A Transformer-Based Multimodal 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[3];Guo, Yuan[2];Ma, Ruixian[4];Zhang, Fan[1];Liu, Yu[1,5]

第一作者：Jia, Furong

通讯作者：Dong, QH[1]

机构：[1]Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China;[2]Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Peoples R China;[3]Beijing Union Univ, Tourism Coll, Beijing 100101, Peoples R China;[4]MIT, Senseable City Lab, Cambridge, MA 02139 USA;[5]Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China

第一机构：Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China

通讯机构：[1]corresponding author), Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China.

年份：2024

卷号：17

起止页码：15041-15051

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);WOS:【SCI-EXPANDED(收录号:WOS:001308275000013)】；

基金：This work was supported in part by the International Research Center of Big Data for Sustainable Development Goals under Grant CBAS2022GSP06 and in part by the National Natural Science Foundation of China under Grant 42401560, Grant 42371468, Grant 42201507, Grant U2344216, and Grant 42271471.

语种：英文

外文关键词：Remote sensing; Transformers; Visualization; Socioeconomics; Buildings; Labeling; Data models; Deep learning; social sensing; street view images (SVIs); urban rural fringe (URF); urbanization

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 multimodal techniques have seldom been explored to integrate multisource data for delineating URFs. To address this gap, this study proposes a transformed-based multimodal 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 km(2) 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. In addition, a series of ablation studies demonstrate the efficacy of incorporating multisource data. Based on the delineated URFs in Beijing, this research introduced points of interest 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 multimodal approach integrating SVIs. Furthermore, it provides a human-centric comprehension of URFs, which is essential for informing strategies of urban planning and development.