Transfer learning with convolutional neural networks for hydrological streamline delineation
Links
- More information: Publisher Index Page (via DOI)
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
Abstract
Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline delineation. We evaluate the performance of eleven image-based pre-trained models and a baseline model using datasets from Rowan County, North Carolina, and Covington River, Virginia in the USA. Our results demonstrate that when models are adapted to a new area, the fine-tuned ImageNet pre-trained model exhibits superior predictive accuracy, markedly higher than the models trained from scratch or those only fine-tuned on the same area. Moreover, the pre-trained model achieves better smoothness and connectivity between classified streamline channels. These findings underline the effectiveness of transfer learning in enhancing the delineation of hydrological streamlines across varied geographies, offering a scalable solution for accurate and efficient environmental modelling.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Transfer learning with convolutional neural networks for hydrological streamline delineation |
| Series title | Environmental Modelling and Software |
| DOI | 10.1016/j.envsoft.2024.106165 |
| Volume | 181 |
| Year Published | 2024 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Center for Geospatial Information Science (CEGIS) |
| Description | 106165, 13 p. |
| Country | United States |
| State | North Carolina, Virginia |