The number of abandoned buildings in shrinking cities is increasing sharply, posing environment risks, threatening the safety and health of residents, affecting the real estate market, and burdening government finance. Abandoned building detection provides fundamental information for refined urban management, real estate transactions and government decision-making. However, emerging sources of data, such as satellite imagery and commercial street views, are insufficient to timely collect this fine-scale data, lacking large-scale and fine-grained detection method. Therefore, in this research, we aim to define the connotation and identification criteria of abandoned buildings, develop an effective deep learning method based on image segmentation, and detect individual abandoned buildings from large-scale mobile sensing images (MSIs) with high accuracy. The study conducted a mobile sensing campaign in a shrinking city in Northeast China, collecting 11,359 street-level images of 126.2 km of urban roads. The accuracy of the deep learning detection method was 83.8%. The study compared with the detection of commercial street view images (latest in 2015) and analyzed the dynamic changes of abandoned buildings. From 2015 to 2021, the number of abandoned buildings in the case city decreased from 102 to 50 and became more concentrated in the old city area. Our study demonstrates the feasibility of MSIs in detecting abandoned buildings and shows the enormous potential to timely detect abandoned buildings in large spatial ranges.
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