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: 1940091
• 负责人: Qunying Huang
• 金额: $ 16.32万
• 依托单位: University of Wisconsin-Madison
• 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=1940091&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.

由于对领域专业知识，培训数据和现场调查的要求，传统的洪水破坏评估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评估洪水灾难期间的实时评估运输网络弹性变得更加具有挑战性。为了应对这些挑战，该项目旨在整合来自物理传感器网络的新颖数据流（例如，使用无人机[UAVS]（例如，公民传感器网络）（例如，人拥挤的交通数据，社交媒体，社交媒体和通过开发的移动应用程序连接的社区响应团队））和公民传感器网络（例如，远程敏感的数据）。目的是通过最先进的计算机视觉，深度学习和数据融合技术来实时评估损害的实时评估和城市运输基础设施的弹性。该项目还将通过多学科和多机构合作来推进数据科学。预计该项目将通过直接影响使用物理和社会传感器数据的有效性，能力和潜力来提高沿海社区的可持续性，韧性，宜居性和一般福祉。反过来，这将实现并改变损害评估，并以更有效和有效的方式确定运输网络中的关键和脆弱的组成部分。跨学科研究团队以及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环境中的知识和技术的共享，并测试研究成果的可转移性。该项目将通过对地理性人工智能（GEOAI）数据框架（GEOAI）的范围内的范围内的物理和公民传感器协调三个研究的范围（GEOAI）的范围，这是（1（1）（1（1）（1（1）（GEOAI）的范围，将三个集成在三个研究中（1（1）（1（1）（1（1（1）在整个项目中收集的无人机图像，并使用现有的地理空间数据（例如，道路网络和建筑足迹）； （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的洪水深度估算； （3）根据近乎实时洪水和损坏信息评估对运输网络的影响和弹性的评估。创新方法将通过合作努力来证明和部署，以应对未来的洪水事件以及几次历史风暴。该项目将生产开源算法，用于未来的教育用途，原始和处理的数据集以及相关的处理软件，一种移动应用程序，可用于吸引社区响应式科学团队和三个研究出版物。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的人。

项目成果

Urban Flood Mapping with Residual Patch Similarity Learning

• DOI: 10.1145/3356471.3365235
• 发表时间: 2019-11
• 期刊: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
• 作者: Bo Peng;Xinyi Liu;Zonglin Meng;Qunying Huang

Flood Depth Estimation from Web Images

• DOI: 10.1145/3356395.3365542
• 发表时间: 2019-11
• 期刊: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities
• 作者: Zonglin Meng;Bo Peng;Qunying Huang

Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features

• DOI: 10.1080/17538947.2021.1968048
• 发表时间: 2021-08
• 期刊: International Journal of Digital Earth
• 影响因子: 5.1
• 作者: C. Scheele;Manzhu Yu;Qunying Huang

Spatiotemporal Contrastive Representation Learning for Building Damage Classification

建筑损伤分类的时空对比表示学习

• DOI: 10.1109/igarss47720.2021.9554302
• 发表时间: 2021
• 期刊: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
• 作者: Peng, Bo;Huang, Qunying;Rao, Jinmeng
• 通讯作者: Rao, Jinmeng

Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

• DOI: 10.3390/rs11212492
• 发表时间: 2019-10
• 期刊: Remote. Sens.
• 作者: Bo Peng;Zonglin Meng;Qunying Huang;Caixia Wang