Collaborative Research: OAC Core: Large-Scale Spatial Machine Learning for 3D Surface Topology in Hydrological Applications

传感技术和计算机模拟的快速发展已经在各个科学领域产生了大量的3D表面数据，从高分辨率的地理地形到蛋白质的静电表面。分析这些新兴的3D地表大数据为科学家们提供了一个研究以前不可能解决的问题的机会，例如绘制整个美国大陆详细的地表水流和分布图。尽管具有巨大的变革潜力，但用于分析大量3D表面数据的机器学习工具并不容易获得。该项目旨在填补这一空白，设计了一个新的并行空间机器学习框架的3D表面拓扑结构和实现系统在分布式计算环境中。该系统可以根据卫星图像制作高质量的基于观测的洪水淹没图。与联邦机构合作（例如，美国地质调查局（NOAA），该项目将加强对洪水灾害响应的情况意识，并通过填补模型校准和验证中缺乏观测的差距来提高NOAA国家水模型的洪水预报能力。拟议的软件工具将是开放源码的，以加强广大地球科学界的研究基础设施。教育活动包括课程开发，在K-12夏令营的数据科学研讨会上指导一组高中生，以及在区域科学博览会竞赛中为选定的高中生提供为期一年的项目。该项目将通过对大规模3D表面拓扑建模来增强地形感知，从而改变空间机器学习研究。具体而言，该项目将带来以下网络基础设施创新。首先，该项目将设计一个名为隐马尔可夫轮廓森林的地形感知空间概率模型，该模型通过将异构3D地形的物理约束纳入模型表示中的区域树结构来改进现有的机器学习工具。其次，本计画将探讨一个平行推论架构，借由分解区域内相依性与区域间相依性。最后，该项目将在分布式计算环境中实现所提出的并行学习框架，解决任务划分，负载平衡和动态任务调度相关的挑战。拟议的系统将通过与美国地质调查局和NOAA的合作，部署在现实世界的快速洪水灾害响应和国家水模型的验证和校准中。该奖项反映了NSF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。

Rapid advances in sensing technology and computer simulation have generated vast amounts of 3D surface data in various scientific domains, from high-resolution geographic terrains to electrostatic surfaces of proteins. Analyzing such emerging 3D surface big data provides scientists an opportunity to study problems that were not possible before, such as mapping detailed surface water flow and distribution for the entire continental US. Despite its vast transformative potential, machine learning tools to analyze large volumes of 3D surface data are not readily available. The project aims to fill this gap by designing a novel parallel spatial machine learning framework for 3D surface topology and implementing the system in a distributed computing environment. The system can produce high-quality observation-based flood inundation maps derived from satellite images. In collaboration with federal agencies (e.g., U.S. Geological Survey, NOAA), the project will enhance situational awareness for flood disaster response and improve flood forecasting capabilities of the NOAA National Water Model by filling in the gap of lacking observations in model calibration and validation. The proposed software tools will be open-source to enhance the research infrastructure for the broad geoscience communities. Educational activities include curriculum development, mentoring a group of high school students in data science seminars at K-12 Summer Camps, and year-long projects for selected high school students in regional Science Fair competitions. The project will transform spatial machine learning research by enhancing terrain awareness through modeling large-scale 3D surface topology. Specifically, the project will bring about the following cyberinfrastructure innovations. First, the project will design a topography-aware spatial probabilistic model called hidden Markov contour forest, which advances existing machine learning tools by incorporating physical constraints of heterogeneous 3D terrains into zonal tree structures in the model representation. Second, the project will investigate a parallel inference framework by decomposing both intra-zone dependency and inter-zone dependency. Finally, the project will implement the proposed parallel learning framework in a distributed computing environment by addressing challenges related to task partitioning, load balancing, and dynamic task scheduling. The proposed system will be deployed for real-world rapid flood disaster response and the validation and calibration of the National Water Model through collaboration with the U.S. Geological Survey and NOAA.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.