[HTML][HTML] Automatic Detection of Ditches and Natural Streams from Digital Elevation Models Using Deep Learning

MDST Busarello, AM Ågren, F Westphal… - Computers & …, 2025 - Elsevier
Policies focused on waterbody protection and restoration have been suggested to European
Union member countries for some time, but to adopt these policies on a large scale the …

[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 …

Mapping small watercourses from DEMs with deep learning—exploring the causes of false predictions

C Koski, P Kettunen, J Poutanen, L Zhu, J Oksanen - Remote Sensing, 2023 - mdpi.com
Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important
for many visualization and analysis use cases. Mapping small watercourses with traditional …

[HTML][HTML] Drainage ditch network extraction from lidar data using deep convolutional neural networks in a low relief landscape

L Du, GW McCarty, X Li, X Zhang, MC Rabenhorst… - Journal of …, 2024 - Elsevier
Drainage networks composed of small, channelized ditches are very common in the eastern
United States. These are human-made features commonly constructed for wetland drainage …

Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning

W Lidberg, SS Paul, F Westphal, KF Richter… - Journal of irrigation …, 2023 - ascelibrary.org
Extensive use of drainage ditches in European boreal forests and in some parts of North
America has resulted in a major change in wetland and soil hydrology and impacted the …

Deep learning-enhanced extraction of drainage networks from digital elevation models

X Mao, JK Chow, Z Su, YH Wang, J Li, T Wu… - … Modelling & Software, 2021 - Elsevier
Drainage network extraction is essential for different research and applications. However,
traditional methods have low efficiency, low accuracy for flat regions, and difficulties in …

An attention U-Net model for detection of fine-scale hydrologic streamlines

Z Xu, S Wang, LV Stanislawski, Z Jiang… - … Modelling & Software, 2021 - Elsevier
Surface water is an irreplaceable resource for human survival and environmental
sustainability. Accurate, finely detailed cartographic representations of hydrologic …

GeoAI-based drainage crossing detection for elevation-derived hydrographic mapping

M Edidem, R Li, D Wu, B Rekabdar, G Wang - Environmental Modelling & …, 2025 - Elsevier
The increasing availability of High-Resolution Digital Elevation Models (HRDEMs) allows
accurate delineation of stream and drainage flowlines at the field scale. However, the …

Mapping small watercourses with deep learning–impact of training watercourse types separately

C Koski, P Kettunen, J Poutanen… - AGILE: GIScience …, 2022 - agile-giss.copernicus.org
Deep learning methods for semantic segmentation have shown great potential in
automating mapping of geospatial features, including small watercourses such as streams …

[HTML][HTML] Combining Deep Learning and Hydrological Analysis for Identifying Check Dam Systems from Remote Sensing Images and DEMs in the Yellow River Basin

M Li, W Dai, M Fan, W Qian, X Yang, Y Tao… - International Journal of …, 2023 - mdpi.com
Identifying and extracting check dams is of great significance for soil and water conservation,
agricultural management, and ecological assessment. In the Yellow River Basin, the check …