Lidberg, William
- Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
Abstract
Accurate mapping of ditches is essential for effective hydrological modeling and land management. Traditional methods, such as manual digitization or threshold-based extraction, utilize LiDAR-derived digital terrain model (DTM) data but are labor-intensive and impractical to apply for large-scale applications. Deep learning offers a promising alternative but requires extensive labeled data, often unavailable. To address this, we developed a transfer learning approach using a U-Net model pre-trained on a large high-quality Swedish dataset and fine-tuned on a smaller localized Estonian dataset. The model uses a single-band LiDAR DTM raster as input, minimizing preprocessing. We identified the optimal model configuration by systematically testing kernel sizes and data augmentation. The best fine-tuned model achieved an overall F1 score of 0.766, demonstrating its effectiveness in detecting drainage ditches in training data-scarce regions. Performance varied by land use, with higher accuracy in peatlands (F1 = 0.822) than in forests (F1 = 0.752) and arable land (F1 = 0.779). These findings underscore the model's suitability for large-scale ditch mapping and its adaptability to different landscapes.
Keywords
Drainage network; LiDAR; U-Net; semantic segmentation; transfer learning
Published in
Big Earth Data
2025
Publisher: TAYLOR AND FRANCIS LTD
SLU Authors
UKÄ Subject classification
Geosciences, Multidisciplinary
Formal Methods
Publication identifier
- DOI: https://doi.org/10.1080/20964471.2025.2491177
Permanent link to this page (URI)
https://res.slu.se/id/publ/141719