Tang leads group awarded funding to apply AI to remote hydraulic structure classification

Center for Applied GIScience (CAGIS) Executive Director Dr. Wenwu Tang recently won, along with colleagues Drs. Shen-en Chen, John Diemer and Craig Allan, funding from NC Department of Transportation to study the application of artifical intelligence to the problem of classifying hydralic structures remotely. Researchers will develop a spatially explicit 3D modeling framework and a software package that are based on deep learning, a cutting-edge artificial intelligence approach, for automated and reliable classification of point cloud data of hydraulic structures (DeepHyd). This DeepHyd framework and associated software package will provide substantial support for resolving the big data-driven computational challenge facing the handling of massive terrestrial LiDAR and sonar data.

Technologies to be used in this project include drones, terrestrial LiDAR, bathymetric sonar, and high-performance computing clusters (in CAGIS!). This project will support a bunch of CAGIS graduate students for the development of this deep learning-based 3D modeling framework and software.

2018-2021, NC Department of Transportation, DeepHyd: A Deep Learning-based Artificial Intelligence Approach for the Automated Classification of Hydraulic Structures from LiDAR and Sonar Data, ($396,989) [PIs: Wenwu Tang, Shenen Chen, John Diemer, and Craig Allan]