I-Corps: Artificial Intelligence-Driven Disaster Risk Prediction and Assessment

I-Corps：人工智能驱动的灾害风险预测和评估

• 批准号: 2037607
• 负责人: Youngjib Ham
• 金额: $ 5万
• 依托单位: Texas A&M University
• 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: 2020-07-15 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037607&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; Driven; Disaster

The broader impact/commercial potential of this I-Corps project is the development of a rapid and automated scene understanding technology to intelligently evaluate existing conditions of vulnerability and assess potential disaster risks more proactively and effectively. This technology may help make informed decisions regarding steps to reduce the impacts of disasters. Developing and implementing the technology to reduce disaster risk requires identifying and assessing the sources of potential risk effectively, but a lack of a rapid and automated tools makes practitioners rely on manual inspection. The proposed innovation is to make the current disaster risk assessment process more intelligent and efficient by reducing time-consuming and labor-intensive manual inspection. If the project is completed successfully, the proposed innovation will enhance scientific and technological understanding of rapid and automated visual sensing and analytics for disaster risk prediction and assessment.This I-Corps project is based on the development of the integrated analysis of low-level image features together with high-level semantic models, which enables robust scene understanding and risk prediction in complex environments. By leveraging large-scale data from multimodal visual sensors (e.g., drones, security cameras, etc.), the technology automatically encodes the context of potential disaster risk into machine vision algorithms to identify the elements at risk on video recordings and assess the degree of vulnerability and the elements at risk in complex environments. The system generates site-specific managerial information in an automated manner, which may enhance risk-informed decision-making for disaster mitigation and preparedness, thereby reducing both the level of risk and the impacts of disasters effectively. The proposed innovation may also provide the foundation for further vision-based reasoning for disaster management.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.

I-Corps项目的更广泛的影响/商业潜力是开发一种快速和自动化的场景理解技术，以智能地评估现有的脆弱性条件，并更主动、更有效地评估潜在的灾害风险。这项技术可能有助于就减少灾害影响的步骤作出明智的决定。开发和实施减少灾害风险的技术需要有效地识别和评估潜在风险的来源，但是缺乏快速和自动化的工具使得从业者依赖于人工检查。提出的创新是通过减少耗时和劳动密集型的人工检查，使当前的灾害风险评估过程更加智能和高效。如果该项目成功完成，拟议的创新将加强对灾害风险预测和评估的快速和自动化视觉传感和分析的科学和技术理解。I-Corps项目的基础是对低水平图像特征和高水平语义模型进行综合分析，从而在复杂环境中实现强大的场景理解和风险预测。通过利用来自多模态视觉传感器（如无人机、安全摄像头等）的大规模数据，该技术自动将潜在灾害风险的背景编码为机器视觉算法，以识别视频记录中的风险要素，并评估复杂环境中的脆弱性程度和风险要素。该系统以自动化的方式产生特定场址的管理信息，这可以加强对减灾和备灾的风险知情决策，从而有效地降低风险水平和灾害的影响。提出的创新还可能为灾害管理的进一步基于视觉的推理提供基础。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。