Abstract
Abstract. To solve the problem of collapsing block walls widely used in Japan, this study proposes a method for extracting block walls using 3D point cloud data measured by the Mobile Mapping System (MMS). Unlike conventional methods, this method identifies block walls based on geometric features without relying on MMS trajectory data or deep learning inference results. In addition, the computational load is low and manual correction can be minimized. In our experiments, we used point cloud data collected in urban areas in Japan and achieved a precision of 0.750, recall of 0.810, and F-measure of 0.779. The results demonstrate the effectiveness of this method for automatic extraction of block walls and rapid assessment of collapse risk and are expected to contribute to safety measures in areas with high seismic risk.
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