Accurate mapping of ditches is essential for effective hydrological mod-eling and land management.Traditional methods,such as manual digi-tization or threshold-based extraction,utilize LiDAR-derived digital terrain mo...Accurate mapping of ditches is essential for effective hydrological mod-eling and land management.Traditional methods,such as manual digi-tization 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.展开更多
基金funded by the Estonian Research Agency[grant number PRG1764,PSG841]Estonian Ministry of Education and Research,Centre of Excellence for Sustainable Land Use[TK232]Estonian Environment Agency[grant MULD2]and by the European Union[ERC,WaterSmartLand,101125476]。
文摘Accurate mapping of ditches is essential for effective hydrological mod-eling and land management.Traditional methods,such as manual digi-tization 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.
