Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment
A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point clo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2009-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/9/7/5241/ |
| _version_ | 1811264344735875072 |
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| author | Norbert Pfeifer Martin Rutzinger Bernhard Höfle Andreas Jochem |
| author_facet | Norbert Pfeifer Martin Rutzinger Bernhard Höfle Andreas Jochem |
| author_sort | Norbert Pfeifer |
| collection | DOAJ |
| description | A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An objectbased error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m2. |
| first_indexed | 2024-04-12T20:01:23Z |
| format | Article |
| id | doaj.art-8798a977da074c2596f52ec1efb3da94 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T20:01:23Z |
| publishDate | 2009-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8798a977da074c2596f52ec1efb3da942022-12-22T03:18:31ZengMDPI AGSensors1424-82202009-07-01975241526210.3390/s90705241Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential AssessmentNorbert PfeiferMartin RutzingerBernhard HöfleAndreas JochemA relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An objectbased error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m2.http://www.mdpi.com/1424-8220/9/7/5241/airborne LiDAR3D point cloudroof plane detectionclassificationsegmentationsolar radiationclear sky index |
| spellingShingle | Norbert Pfeifer Martin Rutzinger Bernhard Höfle Andreas Jochem Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment Sensors airborne LiDAR 3D point cloud roof plane detection classification segmentation solar radiation clear sky index |
| title | Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment |
| title_full | Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment |
| title_fullStr | Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment |
| title_full_unstemmed | Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment |
| title_short | Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment |
| title_sort | automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment |
| topic | airborne LiDAR 3D point cloud roof plane detection classification segmentation solar radiation clear sky index |
| url | http://www.mdpi.com/1424-8220/9/7/5241/ |
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