Enhancing High-resolution Terrain Data Model for Improving the Delineation of Multi-scale Hydrological Connectivity

增强高分辨率地形数据模型以改善多尺度水文连通性的勾画

• 批准号: 1951741
• 负责人: Ruopu Li
• 金额: $ 17.07万
• 依托单位: Southern Illinois University at Carbondale
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951741&HistoricalAwards=false
• 关键词: Enhancing; resolution; Terrain; Data; Model

The project is to create a new geospatial and hydrological modeling approach that improves the identification of rivers and streams using high-resolution digital elevation models (HRDEMs). Accurate identification of these river and stream networks is necessary for monitoring environmental process such as the transport of nutrients and aquatic species. The results will create an intelligent hydrological model by combining methods in geographic information sciences, artificial intelligence and cyberinfrastructure that will enable multi-scale analysis of river and stream networks. The model, algorithms, and datasets generated from this project will be freely available to the public and benefit a broad scope of natural resources management activities, such as watershed monitoring, wetland conservation, and aquatic species protection. The results of this work will be incorporated into educational curriculums and disseminated to the broader educational community.There is a critical need for datasets and modeling approaches that can be used to generate hydrologically corrected HRDEMs and delineate hydrologic features like wetlands, road culverts and bridges at a fine scale. To improve the modeling accuracy, this project will develop a geospatial artificial intelligence-hydrological modeling framework that is compatible with HRDEMs. A deep learning model and an image dataset of drainage crossing locations will be developed to identify the locations of hydraulic drainage structures as virtual flow barriers. The effects of hydraulic drainage structures, HRDEM resolutions, and flow direction algorithms on the delineated hydrologic features and their connectivity will be assessed via controlled experiments for an optimal combination. The model will be implemented on high-performance computing clusters to speed up the geocomputation. This project will substantially advance the state-of-the-art of terrain-based hydrologic modeling integrating methods in geographic information science, artificial intelligence, and cyberinfrastructure.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.

该项目旨在创建一种新的地理空间和水文建模方法，使用高分辨率数字高程模型（HRDEM）改进河流和溪流的识别。这些河流和溪流网络的准确识别是必要的监测环境过程，如营养物质和水生物种的传输。研究结果将通过结合地理信息科学、人工智能和网络基础设施的方法，创建一个智能水文模型，从而实现对河流和溪流网络的多尺度分析。该项目生成的模型、算法和数据集将免费提供给公众，并有利于广泛的自然资源管理活动，如流域监测、湿地保护和水生物种保护。这项工作的结果将被纳入教育大纲，并传播到更广泛的教育界。有一个数据集和建模方法，可用于生成水文校正HRDEM和描绘水文功能，如湿地，公路涵洞和桥梁在一个很好的比例是一个迫切的需要。为了提高建模精度，本项目将开发一个与HRDEM兼容的地理空间人工智能水文建模框架。将开发一个深度学习模型和一个排水交叉位置的图像数据集，以识别作为虚拟流动障碍物的水力排水结构的位置。水力排水结构，HRDEM分辨率和流向算法上划定的水文特征和它们的连通性的影响将通过控制实验进行评估，以获得最佳组合。该模型将在高性能计算集群上实现，以加快地理计算。该项目将极大地推进基于地形的水文建模的最新技术，将地理信息科学、人工智能和网络基础设施的方法集成在一起。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。

项目成果

Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations

• DOI: 10.1145/3624062.3624260
• 发表时间: 2023-11
• 期刊: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
• 作者: Yicheng Zhang;Dhroov Pandey;Di Wu;Turja Kundu;Ruopu Li;Tong Shu

Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices

• DOI: 10.1145/3624062.3624258
• 发表时间: 2023-11
• 期刊: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
• 作者: Yuke Li;Jiwon Baik;Md Marufi Rahman;Iraklis Anagnostopoulos;Ruopu Li;Tong Shu

Assessing the impacts of anthropogenic drainage structures on hydrologic connectivity using high‐resolution digital elevation models

• DOI: 10.1111/tgis.12832
• 发表时间: 2021-08
• 期刊: Transactions in GIS
• 影响因子: 2.4
• 作者: S. Bhadra;Ruopu Li;Di Wu;Guangxing Wang;Banafsheh Rekabdar

Classification and Feature Extraction for Hydraulic Structures Data Using Advanced CNN Architectures

使用先进的 CNN 架构对水工结构数据进行分类和特征提取

• DOI: 10.1109/transai51903.2021.00032
• 发表时间: 2021
• 期刊: 2021 Third International Conference on Transdisciplinary AI (TransAI
• 作者: Talafha, Sameerah;Wu, Di;Rekabdar, Banafsheh;Li, Ruopu;Wang, Guangxing
• 通讯作者: Wang, Guangxing

Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

• DOI: 10.1080/15481603.2023.2230706
• 发表时间: 2023-07
• 期刊: GIScience & Remote Sensing
• 影响因子: 6.7
• 作者: Di Wu;Ruopu Li;Banafsheh Rekabdar;Claire Talbert;Michael Edidem;Guangxing Wang