Large-scale urban functional zone mapping by integrating remote sensing images and open social data |
| |
| Authors: | Shouji Du Bo Liu Xiuyuan Zhang Zhijia Zheng |
| |
| Institution: | 1. Institute of Remote Sensing and GIS, Peking University , Beijing, China;2. Institute of Remote Sensing and GIS, Peking University , Beijing, China  https://orcid.org/0000-0002-0764-0225;3. Institute of Remote Sensing and GIS, Peking University , Beijing, China https://orcid.org/0000-0002-9350-9126 |
| |
| Abstract: | ABSTRACT Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping integrating these two types of data is still not be applied. In this study, a novel approach to mapping large-scale UFZs by integrating remote sensing images (RSIs) and open social data is proposed. First, a context-enabled image segmentation method is improved to generate UFZ units by incorporating road vectors. Second, the segmented UFZs are classified by coupling Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). In the classification framework, physical features from RSIs and social attributes from POI (Point of Interest) data are integrated. A case study of Beijing was performed to evaluate the proposed method, and an overall accuracy of 85.9% was achieved. The experimental results demonstrate that the presented method can provide fine-grained UFZs, and the fusion strategy of RSIs and POI data can distinguish urban functions accurately. The proposed method appears to be promising and practical for large-scale UFZ mapping. |
| |
| Keywords: | Urban functional zones land use classification image segmentation very-high-resolution remote sensing images open social data POI |
|
|
![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0002-0764-0225;3. Institute of Remote Sensing and GIS, Peking University , Beijing, China