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

这个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现场理解技术，以智能地评估现有的脆弱性条件，并更主动和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，但缺乏快速和自动化的工具使从业人员依赖人工检查。建议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，拟议的创新将增强对用于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技术理解。这个i-Corps项目基于对低级别图像特征的集成分析以及高级语义模型的开发，从而能够在复杂环境中实现稳健的场景理解和风险预测。通过利用来自多模式视觉传感器(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难风险的上下文编码到机器视觉算法中，以识别视频记录中的风险元素，并评估复杂环境中的脆弱性程度和风险元素。该系统以自动化方式生成特定地点的管理信息，这可以加强减灾和备灾的风险知情决策，从而有效地降低灾害的风险水平和影响。这项拟议的创新还可能为灾害管理的进一步基于愿景的推理提供基础。这一奖项反映了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。

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.