Dynamic monitoring of urban renewal based on multi-source remote sensing and POI data: A case study of Shenzhen from 2012 to 2020

Abstract

Accurate information on the spatiotemporal distribution of urban renewal (UR) is important for sustainable urban development. Due to its complexity, existing studies could not completely describe the land cover types after demolition, and lacked the effective conversion rules to monitor the whole process of UR demolition and reconstruction which made it impossible to obtain high-precision UR extent, demolition time, and reconstruction time. This study proposed an UR monitoring framework by combining Point of Interest, nighttime light RS data, time-series RS data from Google Earth high-resolution and Landsat imageries. The urban vacant land was introduced to supplement the land cover classification system for UR monitoring and extracted by DeepLabv3 semantic segmentation model. The new conversion rules were then generated to track the historical changes in urban land types, and the multi-temporal classification model was applied to extract spatial and temporal characteristics of UR process. Results showed a total of 3,525.55 hm2 UR region were identified in Shenzhen during 2012–2020, and the largest demolition and reconstruction areas were both observed in 2019. The F1 and F2 scores of extracted UR extent, UR demolition time, and UR reconstruction time were larger than 0.72, 0.63 and 0.66, respectively, indicating high overall accuracies. Our proposed framework is important for the UR dynamic monitoring and can provide scientific basis for future urban construction.

Keywords

• Urban Renewal
• Google Earth Engine
• Urban Vacant Land
• Land Cover Change