Article describes how the accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environment management. To address these challenges, the focus of the author's study is on detecting drainage crossings through the application of advanced convolutional neural networks.
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Article describes how the accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environment management. To address these challenges, the focus of the author's study is on detecting drainage crossings through the application of advanced convolutional neural networks.
Physical Description
9 p.
Notes
Abstract: The accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environmental management. While recent studies have leveraged the spatial characteristics of hydrologic features, the use of elevation models for identifying drainage paths can be influenced by flow barriers. To address these challenges, our focus in this study is on detecting drainage crossings through the application of advanced convolutional neural networks (CNNs). In pursuit of this goal, we use neural architecture search to automatically explore CNN models for identifying drainage crossings. Our approach not only attains high accuracy (over 97% for average precision) in object detection but also excels in efficiently inferring correct drainage crossings within a remarkably short time frame (0.268 ms). Furthermore, we perform a detailed profiling of our approach on GPU systems to analyze performance bottlenecks.
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, 2023, Association for Computing Machinery, November 2023, pp. 1-9
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, November 12-17, 2023. Denver, CO, United States
Publication Title:
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
Volume:
2023
Page Start:
1780
Page End:
1788
Peer Reviewed:
Yes
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Zhang, Yicheng; Pandey, Dhroov; Wu, Di; Kundu, Turja; Li, Ruopu & Shu, Tong.Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations,
article,
November 12, 2023;
(https://digital.library.unt.edu/ark:/67531/metadc2288882/:
accessed June 9, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Engineering.
