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

合作研究：OAC CORE：水文应用中 3D 表面拓扑的大规模空间机器学习

• 批准号: 2152085
• 负责人: Zhe Jiang
• 金额: $ 26.12万
• 依托单位: University of Florida
• 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: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152085&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; CORE; Large

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.

传感技术和计算机模拟的快速进步已经在各种科学领域中产生了大量的3D表面数据，从高分辨率地理地形到蛋白质的静电表面。分析这种新兴的3D表面大数据为科学家提供了研究以前无法使用的问题的机会，例如绘制整个美国大陆的详细地表水流和分布。尽管具有巨大的变革潜力，但无法容易获得分析大量3D表面数据的机器学习工具。该项目旨在通过设计一个新型的平行空间机器学习框架来填补这一空白，用于3D表面拓扑并在分布式计算环境中实现系统。该系统可以产生从卫星图像得出的高质量基于观察的洪水淹没图。通过与联邦机构（例如，美国地质调查局，NOAA）合作，该项目将通过填补模型校准和验证中缺乏观察到的空白，从而提高对洪水灾害响应的情境意识，并提高NOAA国家水模型的洪水预测能力。提出的软件工具将是开源的，以增强广泛地球科学社区的研究基础架构。教育活动包括课程开发，指导一群在K-12夏令营的数据科学研讨会上的高中生，以及为区域科学公平竞赛中选定的高中学生提供长达一年的项目。该项目将通过建模大规模3D表面拓扑来提高地形意识来改变空间机器学习研究。具体来说，该项目将带来以下网络基础设施创新。首先，该项目将设计一个称为“隐藏的马尔可夫轮廓森林”的地形感知的空间概率模型，该模型通过将异质3D地形的物理约束纳入模型表示中的区域树结构来推动现有的机器学习工具。其次，该项目将通过分解区域内依赖性和区域间依赖性来研究平行的推理框架。最后，该项目将通过解决与任务分配，负载平衡和动态任务计划相关的挑战，在分布式计算环境中实现所提出的并行学习框架。拟议的系统将用于现实世界中的快速洪水灾难响应，并通过与美国地质调查局和NOAA的合作进行验证和校准。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。

项目成果

An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)

用于在地球图像上绘制洪水范围的高程引导注释工具（演示论文）

• DOI: 10.1145/3557915.3560962
• 发表时间: 2022
• 期刊: SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
• 作者: Adhikari, Saugat;Yan, Da;Sami, Mirza Tanzim;Khalil, Jalal;Yuan, Lyuheng;Joy, Bhadhan Roy;Jiang, Zhe;Sainju, Arpan Man
• 通讯作者: Sainju, Arpan Man

A Hidden Markov Forest Model for Terrain-Aware Flood Inundation Mapping from Earth Imagery

用于根据地球图像进行地形感知洪水淹没绘图的隐马尔可夫森林模型

• 发表时间: 2023
• 期刊: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM
• 作者: Jiang, Zhe;Zhang, Yupu;Adhikari, Saugat;Yan, Da;Sainju, Arpan Man;Jia, Xiaowei;Xie, Yiqun
• 通讯作者: Xie, Yiqun

Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training

• DOI: 10.1145/3481043
• 发表时间: 2022-01
• 期刊: ACM Transactions on Intelligent Systems and Technology (TIST)
• 作者: Wenchong He;Arpan Man Sainju;Zhe Jiang;Da Yan;Yang Zhou

Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery

• DOI: 10.1145/3534678.3539410
• 发表时间: 2022-08
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Wenchong He;Zhenling Jiang;Marcus Kriby;Yiqun Xie;X. Jia;Da Yan;Yang Zhou

Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery

• DOI: 10.1145/3589132.3625591
• 发表时间: 2023-11
• 期刊: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
• 作者: Zelin Xu;Tingsong Xiao;Wenchong He;Yu Wang;Zhe Jiang