Dataset for Transfer Learning with Convolutional Neural Networks for Hydrological Streamline Delineation study

Citation:

Jaroenchai, N., Wang, S., Stanislawski, L. V., Shavers, E., Jiang, Z., Sagan, V., & Usery, E. L. (2024). Transfer learning with convolutional neural networks for hydrological streamline delineation. Environmental Modelling & Software, 181, 106165.

Description:

Training, validation, and testing data of LiDAR-derived feature maps for hydrological streamline detection of Rowan County and Covington watersheds acquired from USGS

Keywords:

hydrologic streamline delineation, geographic information systems, convolutional neural networks, transfer learning



DOI https://doi.org/10.6084/m9.figshare.24512698.v1
Publcation Date 06-11-2023
Title Dataset for Transfer Learning with Convolutional Neural Networks for Hydrological Streamline Delineation study
Author Nattapon Jaroenchai
Lawrence V. Stanislawski
Ethan Shavers
Keywords hydrologic streamline delineation, geographic information systems, convolutional neural networks, transfer learning
Related Publication (Citation) Jaroenchai, N., Wang, S., Stanislawski, L. V., Shavers, E., Jiang, Z., Sagan, V., & Usery, E. L. (2024). Transfer learning with convolutional neural networks for hydrological streamline delineation. Environmental Modelling & Software, 181, 106165.
Note

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