A Remote Sensing Image Technology-Based Method of Detecting Building Defects in Aging Urban Residential Areas

Abstract

This paper focuses on the identification of building diseases in old urban residential communities and reviews the advances of remote sensing technology in building land extraction, three-dimensional modeling, and disease detection. The article points out that existing methods have achieved significant results in macro-level building recognition and three-dimensional modeling, but there are still deficiencies in the refined identification of specific issues for old residential communities, such as high density, complex spectral characteristics, and facade diseases, including cracking, peeling, and water infiltration. To this end, this paper proposes the use of satellite remote sensing for large-scale identification of building land and subsidence screening, leveraging high-resolution drone imagery for fine modeling of individual buildings and surface defect detection, and relying on ground-based sensing technology for high-precision deformation monitoring and long-term tracking of key locations. The aim is to complement the shortcomings of single technologies—such as spectral mixing, building occlusion, and temporal continuity—through the coordinated use of satellite-based broad screening, drone-based detailed inspection, and ground sensor long-term monitoring, thereby improving the efficiency and accuracy of defect identification in old residential areas with high density and complex environments, and providing technical support for urban renewal and dangerous building management.