RAPID Collaborative: Data Driven Post-Disaster Waste and Debris Volume Predictions using Smartphone Photogrammetry App and Unmanned Aerial Vehicles

The goal of this proposal is to leverage photogrammetry from smart phones and unmanned aerial vehicles (UAVs) to automate the quantification of waste debris. In the aftermath of Hurricane Harvey and associated rainfall-induced flooding, a significant volume of waste and debris will be generated, especially in urban areas such as Houston and Beaumont, Texas. The management of post-disaster debris is an important issue faced by local and federal authorities: it contributes a significant portion of disaster management costs, can generate several times the annual waste generation rates of the affected community, and leads to higher expenditures due to error prone initial debris estimations. The current process to predict debris volume is inaccurate and inefficient, as it utilizes qualitative data from visual observation. The results of this study will improve the calibration of the flood debris estimation models by measuring debris generation due to Hurricane Harvey. This will aid in decision-making tools that ultimately will result in faster and more cost-effective debris management operations for future rainfall, tropical storm, and hurricane-induced flood events that continue to impact the Gulf, the US, and elsewhere around the world.