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Urban Physical Disorder (UPD), such as old or abandoned buildings, brokensidewalks, litter, and graffiti, has a negative impact on residents' quality oflife. They can also increase crime rates, cause social disorder, and pose apublic health risk. Currently, there is a lack of efficient and reliablemethods for detecting and understanding UPD. To bridge this gap, we proposeUPDExplainer, an interpretable transformer-based framework for UPD detection.We first develop a UPD detection model based on the Swin Transformerarchitecture, which leverages readily accessible street view images to learndiscriminative representations. In order to provide clear and comprehensibleevidence and analysis, we subsequently introduce a UPD factor identificationand ranking module that combines visual explanation maps with semanticsegmentation maps. This novel integrated approach enables us to identify theexact objects within street view images that are responsible for physicaldisorders and gain insights into the underlying causes. Experimental results onthe re-annotated Place Pulse 2.0 dataset demonstrate promising detectionperformance of the proposed method, with an accuracy of 79.9%. For acomprehensive evaluation of the method's ranking performance, we report themean Average Precision (mAP), R-Precision (RPrec), and Normalized DiscountedCumulative Gain (NDCG), with success rates of 75.51%, 80.61%, and 82.58%,respectively. We also present a case study of detecting and ranking physicaldisorders in the southern region of downtown Los Angeles, California, todemonstrate the practicality and effectiveness of our framework.

UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery