COLLABORATIVE RESEARCH: GI CATALYTIC TRACK: Cyberinfrastructure for Intelligent High-Resolution Snow Cover Inference from Cubesat Imagery

合作研究：GI CATALYTIC Track：根据立方体卫星图像进行智能高分辨率积雪推断的网络基础设施

• 批准号: 1947893
• 负责人: Ziheng Sun
• 金额: $ 4.72万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947893&HistoricalAwards=false
• 关键词: COLLABORATIVE; RESEARCH; GI; CATALYTIC; TRACK

The ability to observe the Earth from space at relevant spatial and temporal scales is key to understanding how hydrological and ecological systems will respond to climate change. In particular, high spatial and temporal resolution (meter scale, daily frequency) observations of snow-covered areas in mountain regions are critical as snow is important for water resources, driving the seasonal hydrological regimes of the Western U.S., with significant impacts on ecological communities. Planet Labs, Inc. (Planet) is a promising new source of commercial Cubesat high-resolution imagery that can be used in environmental science, as it has both high spatial (3.0-4.0 m) and temporal (1-2 day) resolution. This project will develop open-source, cloud-based cyberinfrastructure including an automated pipeline for processing, analyzing and interpreting Planet Cubesat image data using a machine learning approach to infer snow cover at meter-scale resolution. All models and data products will be openly available for use and modification by scientific communities. The project will support the training of students, postdocs and other early-career researchers through training events, special interest groups, and incubator programs. Currently, remotely-sensed snow observations with adequate temporal (daily) resolution are either captured at a spatial scale far too large to be relevant to high-resolution hydrology and ecology studies (e.g. MODIS, 500m) or are appropriate in spatial scale (1-10 m) but have inadequate temporal resolution and are cost-prohibitive (e.g. airborne LiDAR). The recent increase of commercial Earth Observation data with high spatiotemporal resolution may bridge the gap between ground-based and low-resolution satellite observation data. This project will focus on using convolutional neural networks-based models to couple ground and airborne-derived snow observations with Planet imagery in three different montane systems in Washington, California, and Colorado. These sites have very good coverage of ground and airborne snow observations at high resolution (3m) collected by the NASA Airborne Snow Observatory (ASO) and SnowEx missions, which will be used in the training and validation of the models. The project will develop advanced cyberinfrastructure using scalable virtual machines, distributed collaborative architecture, reusable computational frameworks, and replicable machine learning workflows to empower Earth scientists to access, process and generate high-resolution snow products from Cubesat data. The project will adopt open-source strategies and ensure that all data, algorithms, and architecture comply with FAIR data principles and reproducibility and will include training materials that promote the adoption of the infrastructure and tools.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.

在相关的空间和时间尺度上从空间观察地球的能力是了解水文和生态系统如何应对气候变化的关键。特别是，高空间和时间分辨率（仪表尺度，每日频率）对山区覆盖区域的观察至关重要，因为雪对水资源很重要，推动了美国西部的季节性水文制度，对生态社区产生了重大影响。 Planet Labs，Inc。（Planet）是商业立方体高分辨率图像的新来源，可以在环境科学中使用，因为它具有高空间（3.0-4.0 m）和时间（1-2天）分辨率。该项目将开发开源的，基于云的网络基础结构，包括用于处理，分析和解释行星立方体图像数据的自动化管道，使用机器学习方法来推断仪表尺度分辨率的雪覆盖。所有模型和数据产品都将公开可供科学社区使用和修改。该项目将通过培训活动，特殊利益小组和孵化器计划来支持学生，博士后和其他早期研究人员的培训。目前，具有足够时间（每日）分辨率的远程降雪观测值要么以空间尺度捕获太大，因此与高分辨率的水文学和生态学研究（例如MODIS，500m）相关，或者在空间尺度上适当（1-） 10 m），但时间分辨率不足，并且成本良好（例如机载激光雷达）。近期使用高时空分辨率的商业地球观察数据的增加可能会弥合地面和低分辨率卫星观察数据之间的差距。该项目将着重于使用基于卷积神经网络的模型将地面和空中衍生的雪观察与行星图像在华盛顿，加利福尼亚州和科罗拉多州的三种不同的蒙塔尼系统中。这些地点在高分辨率（3M）的高分辨率（3M）上，由NASA空降雪观测站（ASO）和雪地任务收集，对地面和空降雪的观测值非常好，该雪地将用于模型的培训和验证。该项目将使用可扩展的虚拟机，分布式协作体系结构，可重复使用的计算框架以及可复制的机器学习工作流程开发高级网络基础设施，以使地球科学家从COUCESAT数据访问，处理和生成高分辨率的雪产品。该项目将采用开源策略，并确保所有数据，算法和体系结构都符合公平的数据原则和可重复性，并包括培训材料，以促进基础设施和工具的采用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。

项目成果

GeoFairy2: A Cross-Institution Mobile Gateway to Location-Linked Data for In-Situ Decision Making

• DOI: 10.3390/ijgi10010001
• 发表时间: 2020-12
• 期刊: ISPRS Int. J. Geo Inf.
• 作者: Ziheng Sun;L. Di;Sreten Cvetojevic;Zhiqi Yu

A review of Earth Artificial Intelligence

• DOI: 10.1016/j.cageo.2022.105034
• 发表时间: 2022-01-11
• 期刊: COMPUTERS & GEOSCIENCES
• 影响因子: 4.4
• 作者: Sun, Ziheng;Sandoval, Laura;John, Aji
• 通讯作者: John, Aji

Using Geoweaver to Make Snow Mapping Workflow FAIR

使用 Geoweaver 使雪地绘图工作流程公平

• DOI: 10.1109/escience55777.2022.00062
• 发表时间: 2022
• 期刊: 2022 IEEE 18th International Conference on e-Science (e-Science
• 作者: Alnaim, Ahmed;Sun, Ziheng
• 通讯作者: Sun, Ziheng