Automatic Detection of Ditches and Natural Streams from Digital Elevation Models Using Deep Learning
https://doi.org/10.5878/jrex-z325
This data contains the digital elevation models and polyline shapefiles with the location of channels from the 12 study areas used in this study. It also has the code to generate the datasets used to train the deep learning models to detect channels, ditches, and streams, and calculate the topographic indices. The code to train the models is also included, along with the models with the highest performance in 0.5 m resolution. The channels were mapped differently based on their type: ditches were manually digitized based on the visual analysis of some topographic indices and orthophotos obtained from the DEM. Streams were mapped by initially detecting all natural channel heads, then tracing the downstream channels, and finally manually editing them based on orthophotos.
Documentation files
Documentation files
Citation and access
Citation and access
Data access level:
Creator/Principal investigator(s):
Research principal:
Principal's reference number:
- SLU.seksko.2024.4.4.IÄ-1
Data contains personal data:
Yes
Type of personal data:
Names of user accounts indicating who performed certain steps of the data processing
Citation:
Language:
Method and outcome
Method and outcome
Data format/data structure:
Data collection - Computer-based observation
Data collection - Computer-based observation
Mode of collection:
Computer-based observation
Description of the mode of collection:
Professionals from the Swedish Forest Agency manually digitized the ditches within the 12 study areas spread across Sweden based on the hillshade and high-pass median filter obtained from the DEM. Historical photos and current ortophotos (resolution ranging from 0.17-0.5 m), the ditches were manually digitized.
Streams were mapped by initially detecting all natural channel heads, then tracing the downstream channels, and finally manually editing them based on ortophotos.
Geographic coverage
Geographic coverage
Geographic location:
Geographic description:
The data covers 12 study areas spread across Sweden, containing information related to channel type for small water channels. More information with the precise locations can be found at the README.html file.
Administrative information
Administrative information
Funding
Funding
Funding agency:
- Marianne and Marcus Wallenberg Foundation
Opens a new window at ror.org.
ROR
Award title:
WASP-HS
Funding information:
This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program – Humanities and Society (WASP-HS) funded by the Marianne and Marcus Wallenberg Foundation
https://wasp-hs.org
Topic and keywords
Topic and keywords
Swedish Standard Classification of Research Subjects 2025:
INSPIRE topic categories:
Publications
Publications
Citation:
Busarello, M.D.S.T., Ågren, A. M., Westphal, F., Lidberg, W. Automatic Detection of Ditches and Natural Streams from Digital Elevation Models Using Deep Learning, Computers & Geosciences.
ISSN:
Citation:
Paul, S. S., Maher Hasselquist, E., Jarefjäll, A., & Ågren, A. (2023). Virtual landscape-scale restoration of altered channels helps us understand the extent of impacts to guide future ecosystem management. In Ambio (Vol. 52, Issue 1, pp. 182–194). https://doi.org/10.1007/s13280-022-01770-8
SwePub:
Citation:
Lidberg, W., Paul, S. S., Westphal, F., Richter, K.-F., Lavesson, N., Melniks, R., Ivanovs, J., Ciesielski, M., Leinonen, A., & Ågren, A. M. (2023). Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning. In Journal of irrigation and drainage engineering (No. 04022051; Vol. 149, Issue 3). https://doi.org/10.1061/jidedh.ireng-9796
SwePub:
Citation:
Busarello, M. (2025). Mapping small water channels using machine learning. https://doi.org/10.54612/a.1vuvm11qn6
ISBN:
Metadata
Metadata
Version 1
