POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES
Point cloud segmentation is an important first step in categorising a raw point cloud data. This step is necessary in order to better manage the data and generate other derivative products, e.g. 3D GIS or HBIM. The idea presented in this paper involves the use of 2D GIS to help in the segmentation,...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-01-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W9/523/2019/isprs-archives-XLII-2-W9-523-2019.pdf |
| _version_ | 1831695755737300992 |
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| author | A. Murtiyoso P. Grussenmeyer |
| author_facet | A. Murtiyoso P. Grussenmeyer |
| author_sort | A. Murtiyoso |
| collection | DOAJ |
| description | Point cloud segmentation is an important first step in categorising a raw point cloud data. This step is necessary in order to better manage the data and generate other derivative products, e.g. 3D GIS or HBIM. The idea presented in this paper involves the use of 2D GIS to help in the segmentation, classification, as well as (early) semantic annotation of the point cloud. This derives from the fact that in the case of heritage complex sites, often times the site has been previously documented in a 2D GIS often with attributes and entities. We used this 2D data to help in the segmentation of a 3D point cloud, with the added benefit of automatic extraction and annotation of the related semantic information directly to the segmented clusters. Results show that the developed algorithm performs well with TLS data of spread out heritage sites, with a median success rate of 93% and an average rate of 86%. While manual intervention is still inevitable in some parts of the workflow (e.g. creation of the base shapefiles and choice of object segmentation order), the developed algorithm has shown to significantly reduce overall processing time and resources required in terms of segmentation and semantic annotation of a point cloud in the case of heritage complexes. |
| first_indexed | 2024-12-20T13:00:09Z |
| format | Article |
| id | doaj.art-73131ff2cd1a4e008bfe4b14d0ba1c7b |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-20T13:00:09Z |
| publishDate | 2019-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-73131ff2cd1a4e008bfe4b14d0ba1c7b2022-12-21T19:39:56ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-01-01XLII-2-W952352810.5194/isprs-archives-XLII-2-W9-523-2019POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXESA. Murtiyoso0P. Grussenmeyer1Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, FrancePhotogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, FrancePoint cloud segmentation is an important first step in categorising a raw point cloud data. This step is necessary in order to better manage the data and generate other derivative products, e.g. 3D GIS or HBIM. The idea presented in this paper involves the use of 2D GIS to help in the segmentation, classification, as well as (early) semantic annotation of the point cloud. This derives from the fact that in the case of heritage complex sites, often times the site has been previously documented in a 2D GIS often with attributes and entities. We used this 2D data to help in the segmentation of a 3D point cloud, with the added benefit of automatic extraction and annotation of the related semantic information directly to the segmented clusters. Results show that the developed algorithm performs well with TLS data of spread out heritage sites, with a median success rate of 93% and an average rate of 86%. While manual intervention is still inevitable in some parts of the workflow (e.g. creation of the base shapefiles and choice of object segmentation order), the developed algorithm has shown to significantly reduce overall processing time and resources required in terms of segmentation and semantic annotation of a point cloud in the case of heritage complexes.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W9/523/2019/isprs-archives-XLII-2-W9-523-2019.pdf |
| spellingShingle | A. Murtiyoso P. Grussenmeyer POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES |
| title_full | POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES |
| title_fullStr | POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES |
| title_full_unstemmed | POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES |
| title_short | POINT CLOUD SEGMENTATION AND SEMANTIC ANNOTATION AIDED BY GIS DATA FOR HERITAGE COMPLEXES |
| title_sort | point cloud segmentation and semantic annotation aided by gis data for heritage complexes |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W9/523/2019/isprs-archives-XLII-2-W9-523-2019.pdf |
| work_keys_str_mv | AT amurtiyoso pointcloudsegmentationandsemanticannotationaidedbygisdataforheritagecomplexes AT pgrussenmeyer pointcloudsegmentationandsemanticannotationaidedbygisdataforheritagecomplexes |