Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities
The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurat...
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doaj-e065c30436a149898bf87c7144e0e7b02020-11-25T04:02:16ZengMDPI AGRemote Sensing2072-42922020-11-01123726372610.3390/rs12223726Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in CitiesMaría Sánchez-Aparicio0Susana Del Pozo1Jose Antonio Martín-Jiménez2Enrique González-González3Paula Andrés-Anaya4Susana Lagüela5Department of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, SpainDepartment of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, SpainThe use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m<sup>2</sup> for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m<sup>2</sup>. While the use of low-density point clouds (0.5 point/m<sup>2</sup>) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m<sup>2</sup>) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m<sup>2</sup>) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m<sup>2</sup>. Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m<sup>2</sup>, and that higher point densities do not improve the results with the same intensity as they increase computation time.https://www.mdpi.com/2072-4292/12/22/3726LiDARdensitypoint cloudroofsgeometryphotovoltaic energy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
María Sánchez-Aparicio Susana Del Pozo Jose Antonio Martín-Jiménez Enrique González-González Paula Andrés-Anaya Susana Lagüela |
spellingShingle |
María Sánchez-Aparicio Susana Del Pozo Jose Antonio Martín-Jiménez Enrique González-González Paula Andrés-Anaya Susana Lagüela Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities Remote Sensing LiDAR density point cloud roofs geometry photovoltaic energy |
author_facet |
María Sánchez-Aparicio Susana Del Pozo Jose Antonio Martín-Jiménez Enrique González-González Paula Andrés-Anaya Susana Lagüela |
author_sort |
María Sánchez-Aparicio |
title |
Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities |
title_short |
Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities |
title_full |
Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities |
title_fullStr |
Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities |
title_full_unstemmed |
Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities |
title_sort |
influence of lidar point cloud density in the geometric characterization of rooftops for solar photovoltaic studies in cities |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-11-01 |
description |
The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m<sup>2</sup> for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m<sup>2</sup>. While the use of low-density point clouds (0.5 point/m<sup>2</sup>) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m<sup>2</sup>) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m<sup>2</sup>) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m<sup>2</sup>. Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m<sup>2</sup>, and that higher point densities do not improve the results with the same intensity as they increase computation time. |
topic |
LiDAR density point cloud roofs geometry photovoltaic energy |
url |
https://www.mdpi.com/2072-4292/12/22/3726 |
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