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
A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data c...A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data cube construction.This facilitates integration and aggregation of multi-resolution data from various sources to rapidly calculate spatial statistics.We calculated normalized area and compactness for cell geometries from 5 open-source DGGS implementations-Uber H3,Google S2,RiskAware OpenEAGGR,rHEALPix by Landcare Research New Zealand,and DGGRID by Southern Oregon University-to evaluate their suitability for a global-level statistical data cube.We conclude that the rHEALPix and OpenEAGGR and DGGRID ISEA-based DGGS definitions are most suitable for global statistics because they have the strongest guarantee of equal area preservation-where each cell covers almost exactly the same area on the globe.Uber H3 has the smallest shape distortions,but Uber H3 and Google S2 have the largest variations in cell area.However,they provide more mature software library functionalities.DGGRID provides excellent functionality to construct grids with desired geometric properties but as the only implementation does not provide functions for traversal and navigation within a grid after its construction.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.
基金This research has been supported by the Marie Skłodowska-Curie Actions individual fellowship under the Horizon 2020 Programme grant agreement number 795625,grant number MOBERC34 of the Estonian Research Council(ETAG),and the NUTIKAS programme of the Archimedes foundation.The authors are also thankful for technical support from the High Performance Computing Center of the University of Tartu.
文摘A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data cube construction.This facilitates integration and aggregation of multi-resolution data from various sources to rapidly calculate spatial statistics.We calculated normalized area and compactness for cell geometries from 5 open-source DGGS implementations-Uber H3,Google S2,RiskAware OpenEAGGR,rHEALPix by Landcare Research New Zealand,and DGGRID by Southern Oregon University-to evaluate their suitability for a global-level statistical data cube.We conclude that the rHEALPix and OpenEAGGR and DGGRID ISEA-based DGGS definitions are most suitable for global statistics because they have the strongest guarantee of equal area preservation-where each cell covers almost exactly the same area on the globe.Uber H3 has the smallest shape distortions,but Uber H3 and Google S2 have the largest variations in cell area.However,they provide more mature software library functionalities.DGGRID provides excellent functionality to construct grids with desired geometric properties but as the only implementation does not provide functions for traversal and navigation within a grid after its construction.
