Automatic Identification of Potential Renewal Areas in Urban Residential Districts Using Remote Sensing Data and GeoAI

Urban renewal is crucial for fostering revitalization and sustainable development in cities. Accurate identification of renewal areas in urban residential districts is essential for implementing effective renewal strategies. However, existing studies struggle with the automatic large-scale spatial classification of renewal areas due to their inherent complexity. Moreover, complete demolition and reconstruction are not always conducive to long-term sustainability. This study proposes an automated framework to identify potential renewal areas in urban residential districts, using remote sensing data and GeoAI, with Shanghai as a case study. The framework began by establishing classification rules to identify different renewal modes: retention, renewal, and demolition areas. The data labeling process was performed using the Segment Anything Model, followed by a comprehensive identification of different renewal modes through deep learning models, specifically DeepLabv3+. The results showed that (1) a total of 327.18 km² of retention areas, 130.76 km² of renewal areas, and 37.31 km² of demolition areas were automatically identified; (2) retention areas were evenly distributed across the city, while potential renewal areas were concentrated in the central urban districts, and demolition areas were primarily located in the suburban regions. This automatic identification framework significantly enriches the understanding of spatial complexity in urban systems and broadens the application of remote sensing technology in urban studies. The findings provide valuable insights for urban planners, highlighting priority regions for targeted renewal efforts.