AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES
In this paper a method of detecting buildings in dense populated city areas using a three-dimensional model, produced by aerial images, is described. Further to the detection of the outline of the building, we exact information about the buildings height. The study area is the wider centre of Athens...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-09-01
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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-4-W10/11/2018/isprs-archives-XLII-4-W10-11-2018.pdf |
| _version_ | 1818325109937537024 |
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| author | E. Aroni C. Ioannidis |
| author_facet | E. Aroni C. Ioannidis |
| author_sort | E. Aroni |
| collection | DOAJ |
| description | In this paper a method of detecting buildings in dense populated city areas using a three-dimensional model, produced by aerial images, is described. Further to the detection of the outline of the building, we exact information about the buildings height. The study area is the wider centre of Athens, Greece. Our aim is to exact 3D information for large area, in minimum time and minimum cost, in order to support opensource data bases, such as openstreetmap.org. The proposed methodology consists of three main stages. In the first part of the procedure, aerial images are used to produce a point cloud, using the Semi-Global dense matching algorithm. Following, we classify the objects in the point cloud by remote sensing and photogrammetric methods. The classification’s results are divided in three main classes: ground, vegetation and buildings. Having detected the buildings and their complexes we attempt to find the outlines of each separate building, depending on its level; different levels are considered as different buildings. After detecting individual buildings in the point cloud, a polygon is created around their outline. All polygons were compared to the building polygons available on openstreetmap.org, in order to evaluate the results. The number of levels of 100 buildings, in different parts of the city, was measured manually in order to evaluate the Z-dimension’s results, and openstreetmap.org was updated with that information. Further update and combination of the database created in the current process, with the one available on openstreetmap.org is yet under study. |
| first_indexed | 2024-12-13T11:39:16Z |
| format | Article |
| id | doaj.art-6da25f7ce99f41e799b7cd9830f0c718 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-13T11:39:16Z |
| publishDate | 2018-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-6da25f7ce99f41e799b7cd9830f0c7182022-12-21T23:47:41ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-09-01XLII-4-W10111810.5194/isprs-archives-XLII-4-W10-11-2018AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASESE. Aroni0C. Ioannidis1Laboratory of Photogrammetry, School of Rural and Surveying Engineering, National Technical University of Athens, GreeceLaboratory of Photogrammetry, School of Rural and Surveying Engineering, National Technical University of Athens, GreeceIn this paper a method of detecting buildings in dense populated city areas using a three-dimensional model, produced by aerial images, is described. Further to the detection of the outline of the building, we exact information about the buildings height. The study area is the wider centre of Athens, Greece. Our aim is to exact 3D information for large area, in minimum time and minimum cost, in order to support opensource data bases, such as openstreetmap.org. The proposed methodology consists of three main stages. In the first part of the procedure, aerial images are used to produce a point cloud, using the Semi-Global dense matching algorithm. Following, we classify the objects in the point cloud by remote sensing and photogrammetric methods. The classification’s results are divided in three main classes: ground, vegetation and buildings. Having detected the buildings and their complexes we attempt to find the outlines of each separate building, depending on its level; different levels are considered as different buildings. After detecting individual buildings in the point cloud, a polygon is created around their outline. All polygons were compared to the building polygons available on openstreetmap.org, in order to evaluate the results. The number of levels of 100 buildings, in different parts of the city, was measured manually in order to evaluate the Z-dimension’s results, and openstreetmap.org was updated with that information. Further update and combination of the database created in the current process, with the one available on openstreetmap.org is yet under study.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W10/11/2018/isprs-archives-XLII-4-W10-11-2018.pdf |
| spellingShingle | E. Aroni C. Ioannidis AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES |
| title_full | AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES |
| title_fullStr | AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES |
| title_full_unstemmed | AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES |
| title_short | AUTOMATED BUILDING DETECTION IN DENSE POINT CLOUD AND UPDATE OF OPEN SOURCE DATA BASES |
| title_sort | automated building detection in dense point cloud and update of open source data bases |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W10/11/2018/isprs-archives-XLII-4-W10-11-2018.pdf |
| work_keys_str_mv | AT earoni automatedbuildingdetectionindensepointcloudandupdateofopensourcedatabases AT cioannidis automatedbuildingdetectionindensepointcloudandupdateofopensourcedatabases |