An attention U-Net model for detection of fine-scale hydrologic streamlines
Surface water is an irreplaceable resource for human survival and environmental
sustainability. Accurate, finely detailed cartographic representations of hydrologic …
sustainability. Accurate, finely detailed cartographic representations of hydrologic …
[HTML][HTML] Deep learning-enhanced detection of road culverts in high-resolution digital elevation models: Improving stream network accuracy in Sweden
W Lidberg - Journal of Hydrology: Regional Studies, 2025 - Elsevier
Study region Sweden, a mostly forested country with many small forest roads obstructing
topographical modelling of shallow groundwater and streams. Study focus Maps have …
topographical modelling of shallow groundwater and streams. Study focus Maps have …
Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling
K Yoshida, S Pan, J Taniguchi… - Journal of …, 2022 - iwaponline.com
In response to challenges in land cover classification (LCC), many researchers have
experimented recently with classification methods based on artificial intelligence techniques …
experimented recently with classification methods based on artificial intelligence techniques …
Deep learning-enhanced extraction of drainage networks from digital elevation models
Drainage network extraction is essential for different research and applications. However,
traditional methods have low efficiency, low accuracy for flat regions, and difficulties in …
traditional methods have low efficiency, low accuracy for flat regions, and difficulties in …
Transfer learning with convolutional neural networks for hydrological streamline delineation
N Jaroenchai, S Wang, LV Stanislawski… - … Modelling & Software, 2024 - Elsevier
Hydrological streamline delineation is critical for effective environmental management,
influencing agriculture sustainability, river dynamics, watershed planning, and more. This …
influencing agriculture sustainability, river dynamics, watershed planning, and more. This …
Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning
River-level estimation is a critical task required for the understanding of flood events and is
often complicated by the scarcity of available data. Recent studies have proposed to take …
often complicated by the scarcity of available data. Recent studies have proposed to take …
River extraction method of remote sensing image based on edge feature fusion
B Guo, J Zhang, X Li - IEEE Access, 2023 - ieeexplore.ieee.org
The extraction of rivers from remote sensing images is crucial for urban planning and water
resource utilization. To address the low accuracy of traditional methods for extracting rivers …
resource utilization. To address the low accuracy of traditional methods for extracting rivers …
[HTML][HTML] Subsurface drainage pipe detection using an ensemble learning approach and aerial images
Subsurface drainage pipes are commonly used in the Midwestern United States to reduce
excess soil moisture and improve crop yields. However, they are the considerable source of …
excess soil moisture and improve crop yields. However, they are the considerable source of …
A deep learning model for predicting river flood depth and extent
H Hosseiny - Environmental Modelling & Software, 2021 - Elsevier
This paper presents an innovative deep learning (DL) framework to (a) automatically identify
river geometry and flood extent, and (b) predict river flooding depth. To do that, U-Net, an …
river geometry and flood extent, and (b) predict river flooding depth. To do that, U-Net, an …
Adopting deep learning methods for airborne RGB fluvial scene classification
Rivers are among the world's most threatened ecosystems. Enabled by the rapid
development of drone technology, hyperspatial resolution (< 10 cm) images of fluvial …
development of drone technology, hyperspatial resolution (< 10 cm) images of fluvial …