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

飓风伊恩于2022年9月28日作为灾难性的4级飓风在佛罗里达州西南部地区登陆。它产生了大量的碎片，超出了当地废物管理系统的能力。延迟收集公共财产和通行权中的碎片会引起公众健康和安全方面的关切。由于在联邦应急管理局偿还费用之前，受影响的社区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来估计灾害碎片的初始数量，但随着碎片收集过程随着时间的推移，目前的方法并没有更新估计数字。此外，非法倾倒--未经法律许可在公共场所处置不符合条件的碎片--往往导致这种初步估计过时，并使碎片物流规划和报销管理复杂化。这一快速反应研究补助金项目旨在收集在碎片收集行动过程中公共道路上未收集的碎片的短暂数据，以调查非法碎片处置及其社会和环境影响。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，以捕捉一段时间内非法倾倒的废物的数量；(Ii)通过与碎片承包商合作，将非法倾倒的碎片与其相关的收集轨迹联系起来；以及(Iii)进行在线调查，以了解未收集的碎片、碎片处理方法和社区环境的社会和环境影响。不同时间点的碎片体积将通过使用航空图像和顺序调查产生的三维点云数据的机器学习算法来量化。然后，每个路段上非法处置的碎屑数量将通过比较后续时间的碎屑数量与运输商在过去一段时间内收集的碎屑数量来估计。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势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.