Demolished building detection from aerial imagery using deep learning

Su, Shu; Nawata, Takahiko

In this paper, we present a novel approach for demolished building detection using bi-temporal aerial images and building boundary polygon data. The building boundary polygon data can enable the proposed method to distinguish buildings from non-buildings. Moreover, it can enable the exclusion of non-building changes such as those caused by changes in tree cover, roads, and vegetation. The results of demolished building detection can be achieved by using the building-base. The proposed method classifies each building as demolished or undemolished. The architectures, which based on U-Net and VGG19, are implemented for realizing automatic demolished building detection. The result suggested that U-Net is a useful architecture for image classification problems as well as for semantic segmentation tasks. In order to verify the effectiveness of proposed method, the detection performance is evaluated using images of an entire city. The results suggest that the proposed method can accurately detect demolished buildings with a low mis-detection rate and low over-detection rate.

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Zitierform:

Su, Shu / Nawata, Takahiko: Demolished building detection from aerial imagery using deep learning. 2019. Copernicus Publications.

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Rechteinhaber: Shu Su

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