RAPID: Investigating Illegal Dumping During the Course of Debris Collection Operations Following Hurricane Ian

飓风伊恩于2022年9月28日在佛罗里达州西南部登陆，成为灾难性的4级飓风。它产生了大量的碎片，超出了当地废物管理系统的能力。从公共财产和通行权上延迟收集碎片会引起公共健康和安全问题。由于受灾社区在联邦应急管理署偿还之前使用自己有限的资源清除碎片，因此在整个恢复过程中准确估计和跟踪未收集的碎片至关重要。虽然有各种工具可用于估计灾害碎片的初始数量，但目前的方法不能随着碎片收集过程的进行而更新估计。此外，非法倾倒- -未经法律许可在公共场所处置不合格的垃圾- -往往使这种初步估计过时，并使垃圾后勤规划和报销管理复杂化。这项快速反应研究补助金计划旨在收集公共道路上未收集的碎片的临时数据，以调查非法处置碎片及其对社会和环境的影响。该项目将(i)使用无人驾驶飞行器和激光雷达扫描仪对受影响的公共道路进行连续调查，以捕获随时间推移的非法倾倒废物数量；（ii）通过与垃圾承包商合作，将非法倾倒的垃圾与其相关的收集轨迹联系起来；（iii）进行在线调查，以了解未收集的垃圾对社会和环境的影响、垃圾处理方法和社区环境。不同时间点的碎片体积将通过机器学习算法与航拍图像和顺序调查产生的三维点云数据进行量化。然后，通过比较随后时间的碎片体积以及卡车在过去时间内收集的碎片体积，可以估计每个路段上非法处置的碎片体积。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Hurricane Ian made landfall as a catastrophic Category 4 hurricane on September 28, 2022, in the southwestern region of Florida. It generated substantial amounts of debris, exceeding the capacity of local waste management systems. Delayed collection of debris from public property and rights-of-way incurs public health and safety concerns. Since affected communities use their own limited resources for debris removal before the reimbursement by FEMA, it is critical to accurately estimate and keep track of the uncollected debris throughout recovery. While various tools are available to estimate the initial amount of disaster debris, current approaches do not update the estimates as a debris collection process proceeds over time. Further, illegal dumping - the disposal of ineligible debris at public sites without legal permission - often causes such initial estimates to become obsolete and complicates debris logistics planning and reimbursement management. This Grant for Rapid Response Research (RAPID) project aims to collect ephemeral data on uncollected debris on public roadways during the course of debris collection operations in order to investigate illegal debris disposal and its social and environmental impacts. This project will (i) conduct a sequential survey of affected public roadways with unmanned aerial vehicles and a LiDAR scanner to capture the amount of illegally dumped waste over time, (ii) link illegally dumped debris to its relevant collection trajectories through collaboration with debris contractors, and (iii) perform online surveys to understand the social and environmental impacts of uncollected debris, debris disposal methods, and community settings. The volume of debris at different points in time will be quantified through machine learning algorithms with aerial imagery and 3-D point cloud data resulting from the sequential survey. The volume of illegally disposed debris on each roadway section will then be estimated by comparing the debris volumes at subsequent times along with the volume of debris collected by haulers over the elapsed time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.