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

合作研究：OAC 核心：水文应用中 3D 表面拓扑的大规模空间机器学习

• 批准号: 2106461
• 负责人: Da Yan
• 金额: $ 23.88万
• 依托单位: University of Alabama at Birmingham
• 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 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106461&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，从高分辨率地理地形到蛋白质静电表面。分析此类新兴的 3D 地表大数据为科学家提供了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却并不容易获得。该项目旨在通过设计一种新颖的 3D 表面拓扑并行空间机器学习框架并在分布式计算环境中实现该系统来填补这一空白。该系统可以根据卫星图像生成基于观测的高质量洪水淹没图。该项目将与联邦机构（例如美国地质调查局、NOAA）合作，通过填补模型校准和验证中缺乏观测数据的空白，增强洪水灾害响应的态势感知，并提高 NOAA 国家水模型的洪水预报能力。拟议的软件工具将是开源的，以增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群高中生，以及在地区科学博览会竞赛中为选定的高中生开展为期一年的项目。该项目将通过大规模 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，该项目将设计一种称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，该项目将通过分解区域内依赖性和区域间依赖性来研究并行推理框架。最后，该项目将通过解决与任务分区、负载平衡和动态任务调度相关的挑战，在分布式计算环境中实现所提出的并行学习框架。拟议的系统将通过与美国地质调查局和 NOAA 的合作，部署用于现实世界的快速洪水灾害响应以及国家水模型的验证和校准。该奖项反映了 NSF 的法定使命，并通过使用评估被认为值得支持基金会的智力价值和更广泛的影响审查标准。

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: 2022-11
• 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

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

量化和减少地球图像上空间矢量标签的配准不确定性

• 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: He, Wenchong;Jiang, Zhe;Kriby, Marcus;Xie, Yiqun;Jia, Xiaowei;Yan, Da;Zhou, Yang
• 通讯作者: Zhou, Yang

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

训练标签有限的地形表面地球图像分割：基于物理引导图协同训练的半监督方法

• DOI: 10.1145/3481043
• 发表时间: 2022-01-05
• 期刊: ACM Transactions on Intelligent Systems and Technology (TIST)
• 作者: Wenchong He;Arpan Man Sainju;Zhe Jiang;Da Yan;Yang Zhou
• 通讯作者: Yang Zhou

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: 2023-01
• 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