Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment
1
University of Innsbruck, Department of Geography, 6020 Innsbruck, Austria
2
Vienna University of Technology, Institute of Photogrammetry and Remote Sensing, 1040 Vienna, Austria
3
International Institute for Geo-Information Science and Earth Observation, 7500 Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2009, 9(7), 5241-5262; https://doi.org/10.3390/s90705241
Received: 25 May 2009
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Revised: 25 June 2009
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Accepted: 1 July 2009
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Published: 2 July 2009
(This article belongs to the Special Issue LiDAR for 3D City Modeling)
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.
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Keywords:
airborne LiDAR; 3D point cloud; roof plane detection; classification; segmentation; solar radiation; clear sky index
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MDPI and ACS Style
Jochem, A.; Höfle, B.; Rutzinger, M.; Pfeifer, N. Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment. Sensors 2009, 9, 5241-5262. https://doi.org/10.3390/s90705241
AMA Style
Jochem A, Höfle B, Rutzinger M, Pfeifer N. Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment. Sensors. 2009; 9(7):5241-5262. https://doi.org/10.3390/s90705241
Chicago/Turabian StyleJochem, Andreas, Bernhard Höfle, Martin Rutzinger, and Norbert Pfeifer. 2009. "Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment" Sensors 9, no. 7: 5241-5262. https://doi.org/10.3390/s90705241
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