Wildfires damage assessment Via LiDAR

This paper examines the phenomenon of wildfires in California and investigates buildings affected by Woolsey Fire Central Malibu 2018. We focus empirically on machine learning to identify damaged objects from point-cloud data. project includes a literature review with references methods used for wildfire research LiDAR data processing. In this study, researchers trained an existing deep model determine if it offers effective solution extracting objects. Data sources study include retrieved via LidarExplorer tool Kaggle’s 2013–2020 Using two layers building footprints “T-Zone” revealed 907 structures, which 435 were or destroyed based map observations. analysis structure identification supports that can successfully classify wildfires.