CoPe EAGER: Collaborative Research: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets

CoPe EAGER：协作研究：GeoAI 数据融合框架，通过集成复杂的传感器数据集实时评估洪水损失和运输弹性

• 批准号: 1940163
• 负责人: Zheng Fang
• 金额: $ 9.72万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940163&HistoricalAwards=false
• 关键词: CoPe; EAGER; Collaborative; Research; GeoAI

Traditional modeling approaches for flood damage assessment are often labor-intensive and time-consuming due to requirements for domain expertise, training data, and field surveys. Additionally, the lack of data and standard methodologies makes it more challenging to assess transportation network resilience in real-time during flood disasters. To address these challenges, this project aims to integrate novel data streams from both physical sensor networks (e.g., remotely-sensed data using unmanned aerial vehicles [UAVs]), and citizen sensor networks (e.g., crowdsourced traffic data, social media and community responsive teams connected through a developed mobile app). The goal is to develop a framework for real-time assessment of damage and the resilience of urban transportation infrastructures after coastal floods via the state-of-the-art computer vision, deep learning and data fusion technologies. The project will also advance Data Science through multi-disciplinary and multi-institutional collaborations. The project is expected to improve the sustainability, resilience, livability, and general well-being of coastal communities by having a direct impact on the effectiveness, capability, and potential of using both physical and social sensor data. This will in turn enable and transform damage assessments, and identify critical and vulnerable components in transportation networks in a more effective and efficient manner. The interdisciplinary research team, along with students and collaborators from different coastal regions, will facilitate the sharing of knowledge and technologies from different socio-environmental contexts and testing the transferability of the research outcomes.The project will harmonize physical and citizen sensors within a geospatial artificial intelligence (GeoAI) data-fusion framework with a focus on three research thrusts: (1) unsupervised flood extent detection by integrating UAV images collected throughout this project with existing geospatial data (e.g., road networks and building footprints); (2) flood depth estimation using deep learning and computer vision techniques combined with crowdsourced photos and UAV imagery; and (3) assessment of the impact on and resilience of transportation networks based on near real-time flood and damage information. The innovative methodology will be demonstrated and deployed through collaborative efforts in response to future flood events as well as several historical storms. The project will produce open-source algorithms for future educational use, raw and processed datasets and associated processing software, a mobile app to engage community responsive science teams, and three research publications.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.

传统的洪水损失评估建模方法往往是劳动密集型和耗时的，由于领域的专业知识，训练数据和实地调查的要求。此外，由于缺乏数据和标准方法，在洪水灾害期间实时评估交通网络的弹性更具挑战性。为了应对这些挑战，该项目旨在整合来自物理传感器网络（例如，使用无人驾驶飞行器[UAV]的遥感数据），以及公民传感器网络（例如，众包交通数据、通过开发的移动的应用程序连接的信息社交和社区响应团队）。其目标是通过最先进的计算机视觉、深度学习和数据融合技术，开发一个实时评估沿海洪水后城市交通基础设施损害和恢复力的框架。该项目还将通过多学科和多机构合作推进数据科学。该项目预计将通过对使用物理和社会传感器数据的有效性、能力和潜力产生直接影响，提高沿海社区的可持续性、复原力、宜居性和总体福祉。这反过来又将促进和改变损害评估，并以更有效和更高效的方式确定运输网络中的关键和脆弱组成部分。跨学科研究团队沿着来自不同沿海地区的学生和合作者，将促进来自不同社会环境背景的知识和技术的共享，并测试研究成果的可转移性。该项目将在地理空间人工智能（GeoAI）数据融合框架内协调物理和公民传感器，重点关注三个研究方向：（1）通过将整个项目中收集的无人机图像与现有地理空间数据（例如，道路网络和建筑物足迹）;（2）使用深度学习和计算机视觉技术结合众包照片和无人机图像进行洪水深度估计;以及（3）基于近实时洪水和损坏信息评估交通网络的影响和恢复能力。创新的方法将通过合作努力来展示和部署，以应对未来的洪水事件以及几次历史风暴。该项目将产生开源算法用于未来的教育用途，原始和处理的数据集和相关的处理软件，一个移动的应用程序，以吸引社区响应科学团队，和三个研究出版物。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Investigation of Infiltration Loss in North Central Texas by Retrieving Initial Abstraction and Constant Loss from Observed Rainfall and Runoff Events

通过从观测到的降雨和径流事件中检索初始抽取和持续损失来调查德克萨斯州中北部的渗透损失

• DOI: 10.1061/jhyeff.heeng-5883
• 发表时间: 2023
• 期刊: Journal of Hydrologic Engineering
• 影响因子: 2.4
• 作者: Zhang, Jiaqi;Gao, Shang;Fang, Zheng
• 通讯作者: Fang, Zheng

Modeling SARS-CoV-2 RNA degradation in small and large sewersheds

• DOI: 10.1039/d1ew00717c
• 发表时间: 2021-12-28
• 期刊: ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY
• 影响因子: 5
• 作者: McCall, Camille;Fang, Zheng N.;Stadler, Lauren B.
• 通讯作者: Stadler, Lauren B.

Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery

• DOI: 10.3390/hydrology10080158
• 发表时间: 2023-07
• 期刊: Hydrology
• 影响因子: 3.2
• 作者: K. Wienhold;Dongfeng Li;Wenzhao Li;Zheng N. Fang

Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events

• DOI: 10.3390/rs15082033
• 发表时间: 2023-04
• 期刊: Remote. Sens.
• 作者: Wenzhao Li;Han Jiang;Dongfeng Li;P. Bedient;Zheng N. Fang

Evaluation of Multiradar Multisensor and Stage IV Quantitative Precipitation Estimates during Hurricane Harvey

飓风哈维期间多雷达多传感器评估和第四阶段定量降水估算

• DOI: 10.1061/(asce)nh.1527-6996.0000435
• 发表时间: 2021
• 期刊: Natural Hazards Review
• 影响因子: 2.7
• 作者: Gao, Shang;Zhang, Jiaqi;Li, Dongfeng;Jiang, Han;Fang, Zheng N.
• 通讯作者: Fang, Zheng N.