Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation
The study explores deep learning to perform direct semantic segmentation of bathymetric lidar points to improve bathymetry mapping. Focusing on river bathymetry, the goal is to accurately and simultaneously classify points on the benthic layer, water surface, and ground near riverbanks. These classi...
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| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2025.1521446/full |
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| author | Ovi Paul Nima Ekhtari Craig L. Glennie |
| author_facet | Ovi Paul Nima Ekhtari Craig L. Glennie |
| author_sort | Ovi Paul |
| collection | DOAJ |
| description | The study explores deep learning to perform direct semantic segmentation of bathymetric lidar points to improve bathymetry mapping. Focusing on river bathymetry, the goal is to accurately and simultaneously classify points on the benthic layer, water surface, and ground near riverbanks. These classifications are then used to apply depth correction to all points within the water column. The study aimed to classify the scene into four classes: river surface, riverbed, ground, and other (for points outside of those three classes), focusing on the river surface and riverbed classes. To achieve this, PointCNN, a convolutional neural network model adept at handling unorganized and unstructured data in 3D space was implemented. The model was trained with airborne bathymetric lidar data from the Swan River in Montana and the Eel River in California. The model was tested on the Snake River in Wyoming to evaluate its performance. These diverse bathymetric datasets across the United States provided a solid foundation for the model’s robust testing. The results were strong for river surface classification, achieving an Intersection over Union of (0.89) and a Kappa coefficient of (0.92), indicating high reliability and minimal errors. The riverbed classification also showed an IoU of (0.7) and a slightly higher Kappa score of (0.76). Depth correction was then performed on riverbed points, proportional to the calculated depth from a surface model formed by Delaunay triangulation of ground and river surface points. The automated process performs significantly faster than traditional manual classification and depth correction processes, saving time and expense. Finally, corrected depths were quantitatively validated by comparing with independent Acoustic Doppler Current Profiler measurements from the Snake River, obtaining a mean depth error of 2 cm and an Root mean square error of 16 cm. These validation results show the reliability and accuracy of the proposed automated bathymetric depth correction workflow. |
| format | Article |
| id | doaj-art-5d8692e799b84cd9b08ea061a6bbdbd3 |
| institution | Kabale University |
| issn | 2673-6187 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj-art-5d8692e799b84cd9b08ea061a6bbdbd32025-08-20T03:36:42ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-08-01610.3389/frsen.2025.15214461521446Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentationOvi PaulNima EkhtariCraig L. GlennieThe study explores deep learning to perform direct semantic segmentation of bathymetric lidar points to improve bathymetry mapping. Focusing on river bathymetry, the goal is to accurately and simultaneously classify points on the benthic layer, water surface, and ground near riverbanks. These classifications are then used to apply depth correction to all points within the water column. The study aimed to classify the scene into four classes: river surface, riverbed, ground, and other (for points outside of those three classes), focusing on the river surface and riverbed classes. To achieve this, PointCNN, a convolutional neural network model adept at handling unorganized and unstructured data in 3D space was implemented. The model was trained with airborne bathymetric lidar data from the Swan River in Montana and the Eel River in California. The model was tested on the Snake River in Wyoming to evaluate its performance. These diverse bathymetric datasets across the United States provided a solid foundation for the model’s robust testing. The results were strong for river surface classification, achieving an Intersection over Union of (0.89) and a Kappa coefficient of (0.92), indicating high reliability and minimal errors. The riverbed classification also showed an IoU of (0.7) and a slightly higher Kappa score of (0.76). Depth correction was then performed on riverbed points, proportional to the calculated depth from a surface model formed by Delaunay triangulation of ground and river surface points. The automated process performs significantly faster than traditional manual classification and depth correction processes, saving time and expense. Finally, corrected depths were quantitatively validated by comparing with independent Acoustic Doppler Current Profiler measurements from the Snake River, obtaining a mean depth error of 2 cm and an Root mean square error of 16 cm. These validation results show the reliability and accuracy of the proposed automated bathymetric depth correction workflow.https://www.frontiersin.org/articles/10.3389/frsen.2025.1521446/fullbathymetric lidardeep learning3D convolutional neural networkgraph neural networksemantic segmentationPointCNN |
| spellingShingle | Ovi Paul Nima Ekhtari Craig L. Glennie Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation Frontiers in Remote Sensing bathymetric lidar deep learning 3D convolutional neural network graph neural network semantic segmentation PointCNN |
| title | Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation |
| title_full | Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation |
| title_fullStr | Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation |
| title_full_unstemmed | Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation |
| title_short | Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation |
| title_sort | automated depth correction of bathymetric lidar point clouds using pointcnn semantic segmentation |
| topic | bathymetric lidar deep learning 3D convolutional neural network graph neural network semantic segmentation PointCNN |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2025.1521446/full |
| work_keys_str_mv | AT ovipaul automateddepthcorrectionofbathymetriclidarpointcloudsusingpointcnnsemanticsegmentation AT nimaekhtari automateddepthcorrectionofbathymetriclidarpointcloudsusingpointcnnsemanticsegmentation AT craiglglennie automateddepthcorrectionofbathymetriclidarpointcloudsusingpointcnnsemanticsegmentation |