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- W2077598465 abstract "There is a growing need for rapid and accurate damage and debris assessment following natural disasters, terroristattacks, and other crisis situations. This research enhances existing algorithms for LiDAR point classification(ground/non-ground), feature classification (buildings, vegetation, roads, etc.), and seeks to develop new algorithms forbuilding damage and debris detection and quantification-work evaluated using LiDAR data of Port-au-Prince, Haiti,collected by RIT just days after the January 12, 2010 earthquake. Normalized height, height variation, intensity, andmultiple return information are among the parameters being used to develop rules for building extraction and vegetationremoval. Various approaches are being explored to perform damage assessment, with a focus on the slope and texture ofroof planes. Initial results show a general over-segmentation in a 457x449 m region-of-interest (ROI)-the buildingdetection algorithm autonomously identified 206 buildings, while only 98 buildings actually exist in the ROI. Further,four buildings went completely undetected. The accuracy of the damage detection algorithm was assessed only inregions where the building detection algorithm results overlapped actual building locations. The overall damagedetection accuracy was 73.40%, but with a low Kappa accuracy of k = 0.275. The algorithms will be implemented in acommon programming language where the processing will be optimized for large data sets. The goal is for theoperational tool to be implemented in the field, using available equipment in a close to real-time environment." @default.
- W2077598465 created "2016-06-24" @default.
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- W2077598465 date "2011-05-13" @default.
- W2077598465 modified "2023-09-27" @default.
- W2077598465 title "Automated method for detection and quantification of building damage and debris using post-disaster lidar data" @default.
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- W2077598465 doi "https://doi.org/10.1117/12.883509" @default.
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