Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation
Research regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small are...
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MDPI AG
2025-08-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/15/2731 |
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| author | Agata Walicka Norbert Pfeifer |
| author_facet | Agata Walicka Norbert Pfeifer |
| author_sort | Agata Walicka |
| collection | DOAJ |
| description | Research regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small area. Recently, many European countries published classified ALS data, which can be potentially used for training models. However, a review of the classification schemes of these datasets revealed that these schemes vary substantially, therefore limiting their applicability. Thus, our goal was three-fold. First, to develop a common classification scheme that can be applied for the semantic segmentation of various ALS datasets. Second, to unify the classification scheme of existing ALS datasets. Third, to employ them for the training of a classifier that will be able to classify data from different sources and will not require additional training. We propose a classification scheme of four classes: ground and water, vegetation, buildings and bridges, and ‘other’. The developed classifier is trained jointly using ALS data from Austria, Switzerland, and Poland. A test on unseen datasets demonstrates that the achieved intersection over union accuracy varies between 90.0–97.3% for ground and water, 68.0–95.9% for vegetation, 77.6–94.8% for buildings and bridges, and 13.5–52.7% for ‘other’. As a result, we conclude that the developed method generalizes well to previously unseen data. |
| format | Article |
| id | doaj-art-dea1663e4de142a79fbca95e8b9b7ae6 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dea1663e4de142a79fbca95e8b9b7ae62025-08-20T03:36:30ZengMDPI AGRemote Sensing2072-42922025-08-011715273110.3390/rs17152731Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First ImplementationAgata Walicka0Norbert Pfeifer1Institute of Geodesy and Geoinformatics, Wrocław University of Environmental and Life Sciences, 50-375 Wrocław, PolandDepartment of Geodesy and Geoinformation, Technische Universität Wien, 1040 Vienna, AustriaResearch regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small area. Recently, many European countries published classified ALS data, which can be potentially used for training models. However, a review of the classification schemes of these datasets revealed that these schemes vary substantially, therefore limiting their applicability. Thus, our goal was three-fold. First, to develop a common classification scheme that can be applied for the semantic segmentation of various ALS datasets. Second, to unify the classification scheme of existing ALS datasets. Third, to employ them for the training of a classifier that will be able to classify data from different sources and will not require additional training. We propose a classification scheme of four classes: ground and water, vegetation, buildings and bridges, and ‘other’. The developed classifier is trained jointly using ALS data from Austria, Switzerland, and Poland. A test on unseen datasets demonstrates that the achieved intersection over union accuracy varies between 90.0–97.3% for ground and water, 68.0–95.9% for vegetation, 77.6–94.8% for buildings and bridges, and 13.5–52.7% for ‘other’. As a result, we conclude that the developed method generalizes well to previously unseen data.https://www.mdpi.com/2072-4292/17/15/2731ALSclassificationdeep learningnational LiDARpoint cloudsemantic segmentation |
| spellingShingle | Agata Walicka Norbert Pfeifer Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation Remote Sensing ALS classification deep learning national LiDAR point cloud semantic segmentation |
| title | Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation |
| title_full | Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation |
| title_fullStr | Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation |
| title_full_unstemmed | Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation |
| title_short | Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation |
| title_sort | standard classes for urban topographic mapping with als classification scheme and a first implementation |
| topic | ALS classification deep learning national LiDAR point cloud semantic segmentation |
| url | https://www.mdpi.com/2072-4292/17/15/2731 |
| work_keys_str_mv | AT agatawalicka standardclassesforurbantopographicmappingwithalsclassificationschemeandafirstimplementation AT norbertpfeifer standardclassesforurbantopographicmappingwithalsclassificationschemeandafirstimplementation |