A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-scale roof characterization from LiDAR data and aerial orthoimagery: Automatic computation of building photovoltaic capacity
Abstract Photovoltaic self-consumption in buildings requires the installation of photovoltaic (PV) systems mostly on roofs, taking the advantage of these building locations for both the available surface area and the certainty of a big amount of annual incoming solar radiation. Since the parameters required for a proper PV system design are mainly geometric: azimuth-orientation, tilt angle and effective dimensions of the different roof slopes; aerial LiDAR data and orthoimagery offered by the National Geographic Agencies became suitable data sources for this purpose, ensuring its availability for any city regardless of its location. This paper presents a novel automatic methodology that combines LiDAR and orthoimage data processing to geometrically characterize roofs at slope level and calculate their PV solar potential. The methodology developed has been validated against results obtained from a higher-resolution aerial 3D point cloud of the roofs under study. Different locations and roof types have been tested in order to confirm the performance of the methodology under different conditions, being able to accurately characterize the geometry of most types of roofs, such as flat roofs, gable or saddle roofs, single pitched roofs and pyramid roofs at city, neighbourhood and building level.
Highlights Roof angular and dimension parameters are key for the computation of PV potential. Aerial LiDAR and orthoimagery are valid data sources for roof studies. Combined use of LiDAR and orthoimagery provides high-quality roof characterization. Methodology developed for PV studies at city and roof levels. Standard data presents same quality results as detailed aerial data.
Multi-scale roof characterization from LiDAR data and aerial orthoimagery: Automatic computation of building photovoltaic capacity
Abstract Photovoltaic self-consumption in buildings requires the installation of photovoltaic (PV) systems mostly on roofs, taking the advantage of these building locations for both the available surface area and the certainty of a big amount of annual incoming solar radiation. Since the parameters required for a proper PV system design are mainly geometric: azimuth-orientation, tilt angle and effective dimensions of the different roof slopes; aerial LiDAR data and orthoimagery offered by the National Geographic Agencies became suitable data sources for this purpose, ensuring its availability for any city regardless of its location. This paper presents a novel automatic methodology that combines LiDAR and orthoimage data processing to geometrically characterize roofs at slope level and calculate their PV solar potential. The methodology developed has been validated against results obtained from a higher-resolution aerial 3D point cloud of the roofs under study. Different locations and roof types have been tested in order to confirm the performance of the methodology under different conditions, being able to accurately characterize the geometry of most types of roofs, such as flat roofs, gable or saddle roofs, single pitched roofs and pyramid roofs at city, neighbourhood and building level.
Highlights Roof angular and dimension parameters are key for the computation of PV potential. Aerial LiDAR and orthoimagery are valid data sources for roof studies. Combined use of LiDAR and orthoimagery provides high-quality roof characterization. Methodology developed for PV studies at city and roof levels. Standard data presents same quality results as detailed aerial data.
Multi-scale roof characterization from LiDAR data and aerial orthoimagery: Automatic computation of building photovoltaic capacity
Martín-Jiménez, J. (author) / Del Pozo, S. (author) / Sánchez-Aparicio, M. (author) / Lagüela, S. (author)
2019-09-11
Article (Journal)
Electronic Resource
English
Investigation on roof segmentation for 3D building reconstruction from aerial LIDAR point clouds
| BASE | 2019
AUTOMATIC RECONSTRUCTION OF ROOF MODELS FROM BUILDING OUTLINES AND AERIAL IMAGE DATA
| BASE | 2019