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

由于对领域专业知识、培训数据和实地调查的要求，用于洪水损失评估的传统建模方法通常是劳动密集型和耗时的。此外，缺乏数据和标准方法使洪水灾害期间实时评估交通网络弹性变得更具挑战性。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，使用无人机的遥感数据)和公民传感器网络(例如，通过开发的移动应用程序连接的众包交通数据、社交媒体和社区响应团队)的新型数据流。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，开发一个框架，用于实时评估沿海洪灾后城市交通基础设施的损害和恢复能力。该项目还将通过多学科和多机构合作推动数据科学的发展。该项目预计将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，从而改善沿海社区的可持续性、复原力、宜居性和总体福祉。这将反过来支持和改变损害评估，并以更有效和高效的方式确定运输网络中的关键和脆弱组件。跨学科研究小组与来自不同沿海地区的学生和合作者一起，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，重点放在三个研究重点上：(1)通过将整个项目收集的无人机图像与现有的地理空间数据(例如，道路网络和建筑物足迹)相结合，进行无人监督的洪水范围检测；(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，估计洪水深度；以及(3)基于接近实时的洪水和破坏信息评估交通网络的影响和恢复能力。将通过合作努力，展示和部署这一创新方法，以应对未来的洪水事件和几次历史性风暴。该项目将生产用于未来教育用途的开源算法、原始和经过处理的数据集和相关处理软件、一个参与社区响应科学团队的移动应用程序，以及三个研究出版物。该奖项反映了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。

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.