SBIR Phase I: A Physics Guided Machine Learning Framework for Monitoring Rivers using Satellite Imagery

SBIR 第一阶段：使用卫星图像监测河流的物理引导机器学习框架

• 批准号: 2045444
• 负责人: Ankush Khandelwal
• 金额: $ 25.59万
• 依托单位: TERRA COVER, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045444&HistoricalAwards=false
• 关键词: SBIR; Phase; Physics; Guided; Machine

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to provide near-real time information of changes in the spatial extent (flood mapping) and flow of rivers (water resource management) to insurance, energy, and agricultural stakeholders. Effective management of water resources and associated risks has become a major challenge for society. Floods are common disasters around the world and droughts lead to major disruptions to economies and a loss of life. This project will leverage artificial intelligence, and peta-bytes of satellite imagery to implement a physics guided data-intensive approach for advancing global hydrological modeling. The project will provide efficient and accurate imagery-derived observations of water dynamics in rivers at relatively low computational cost (compared with ground sensors) in a user-friendly web environment. This will be a significant step towards improving the modelling and forecasting of water resources around the world.This Small Business Innovation Research (SBIR) Phase I project aims to develop advanced artificial intelligence techniques to track surface water changes in rivers across the globe using vast amounts of satellite imagery. While conventional artificial intelligence techniques are purely driven by data, the proposed technology incorporates known physical laws into these algorithms. This physics guided approach makes these techniques much more robust to atmospheric disturbances (clouds, shadows, haze, etc.), and enables synergistic use of imagery datasets at different resolutions which are two major issues with satellite imagery analysis. Furthermore, the proposed uncertainty quantification techniques will enable domain experts to incorporate their local knowledge about river flows into the framework to refine results.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响将是在空间范围内（洪水映射）和河流（水资源管理）（水资源管理）对保险，能源和农业利益相关者的近乎真实的时间信息。有效管理水资源和相关风险已成为社会的主要挑战。洪水是世界各地常见的灾难，干旱导致对经济和生命损失的重大破坏。该项目将利用人工智能，以及卫星图像的PETA副词来实施物理指导的数据密集型方法，以推进全球水文建模。该项目将在用户友好的网络环境中以相对较低的计算成本（与地面传感器相比）提供有效，准确的图像衍生的水动力学观察结果。这将是改善世界各地水资源建模和预测的重要一步。这项小型企业创新研究（SBIR）I阶段项目旨在使用大量卫星图像来开发先进的人工智能技术，以跟踪全球河流中的地表水变化。尽管常规人工智能技术纯粹由数据驱动，但拟议的技术将已知的物理定律纳入这些算法中。这种物理学指导的方法使这些技术对大气干扰（云，阴影，雾霾等）更加强大，并在不同的分辨率下可以协同使用图像数据集，这是卫星图像分析的两个主要问题。此外，提出的不确定性量化技术将使领域专家能够将他们关于河流流的本地知识纳入框架以完善结果。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响来评估通过评估来支持。