Detection of drainage ditches from LiDAR DTM using U-Net and transfer learning

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.