Post-Hurricane Debris and Community Flood Damage Assessment Using Aerial Imagery

Abstract

Highlights: What are the main findings? Using a combination of machine learning and non-machine learning tools on aerial imagery to provide rapid estimation of debris in lakes, land, and near streams, with accuracy sufficient for preliminary disaster assessments. Flood modeling can be used to rapidly assess damage in a community with limited ground truth data, with errors less than 8% demonstrated for a small Appalachian community. What is the implication of the main finding? Proposed methods for debris estimation from aerial imagery offer more efficient alternatives to slow, labor-intensive visual inspection and ground-based methods, improving the timeliness and accuracy of recovery planning. Validated flood modeling can support proactive community-level damage classification before field surveys, accelerating disaster response decisions. Natural disasters often result in significant damage to infrastructure, generating vast amounts of debris in towns and water bodies. Timely post-disaster damage assessment is critical for enabling swift cleanup and recovery efforts. This study presents a combination of methods to efficiently estimate and analyze debris on land and on water. Specifically, analyses were conducted at Claytor Lake and Damascus, Virginia where flooding occurred as a result of Hurricane Helene on 27 September 2024. We use the Phoenix U15 motor glider equipped with the GoPro Hero 9 camera to collect aerial imagery. Orthomosaic images and 3D maps are generated using OpenDroneMap (ODM) software, version 3.5.6, providing a detailed view of the affected areas. For lake debris estimation, we employ a hybrid approach integrating machine learning-based tools and traditional techniques. Lake regions are isolated using segmentation methods, and the debris area is estimated through a combination of color thresholding and edge detection. The debris is classified based on the thickness and a volume range of debris is presented based on the data provided by the Virginia Department of Environmental Quality (VDEQ). In Damascus, debris estimation is achieved by comparing pre-disaster LiDAR data (2016) with post-disaster 3D ODM data. Furthermore, we conduct flood modeling using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) to simulate disaster impacts, estimate the flood water depth, and support urban planning efforts. The proposed methodology demonstrates the ability to deliver accurate debris estimates in a time-sensitive manner, providing valuable insights for disaster management and environmental recovery initiatives.