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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响将是向保险、能源和农业提供空间范围（洪水测绘）和河流流量（水资源管理）变化的近乎实时信息。利益相关者。有效管理水资源和相关风险已成为社会面临的重大挑战。洪水是世界各地常见的灾害，干旱会导致经济严重破坏和人员伤亡。该项目将利用人工智能和 PB 级卫星图像来实施物理引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）提供高效、准确的河流水动态图像观测。这将是朝着改善全球水资源建模和预测迈出的重要一步。这个小型企业创新研究（SBIR）第一阶段项目旨在开发先进的人工智能技术，利用大量数据来跟踪全球河流地表水的变化卫星图像。虽然传统的人工智能技术纯粹由数据驱动，但所提出的技术将已知的物理定律融入到这些算法中。这种物理引导方法使这些技术对大气扰动（云、阴影、雾霾等）更加鲁棒，并且能够协同使用不同分辨率的图像数据集，这是卫星图像分析的两个主要问题。此外，所提出的不确定性量化技术将使领域专家能够将他们有关河流流量的当地知识纳入框架中以完善结果。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查进行评估，被认为值得支持标准。