Identifying Urban Building Function by Integrating Remote Sensing Imagery and POI Data

• Main Authors: Anqi Lin, Xiaomeng Sun, Hao Wu, Wenting Luo, Danyang Wang, Dantong Zhong, Zhongming Wang, Lanting Zhao, Jiang Zhu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Google earth image; kernel density estimation (KDE); point of interest (POI) data; spatial similarity; urban buildings; user generate contents (UGCs)
• Online Access: https://ieeexplore.ieee.org/document/9524442/

Identifying urban building function plays a critical role in understanding the complexness of urban construction and improving the effectiveness of urban planning. The emergence of user generated contents has brought access to massive semantic information which complements the traditional remote sensing data for identifying urban building functions and exploring the spatial structure in urban environment. This article proposes a stepwise identification framework for urban building functions based on remote sensing imagery and point of interests (POIs) data, which merges the spatial similarity of buildings and kernel density to improve the identification accuracy and completeness. Taking Wuhan as an example, Google earth images and POI data were obtained to identify the seven primary categories for the individual buildings in the core urban area. The results suggest that the proposed stepwise framework is feasible to identify the urban building functions as the identification results exhibit the superiority in terms of accuracy and completeness. Our results suggest that the identification of urban building function is sensitive to the bandwidth of kernel density estimation and 200 meter is the optimal size. The findings also indicate that significant spatial agglomeration exists in residential and commercial buildings at both macro and microlevels.