Volume XL-5/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W4, 171-178, 2015
https://doi.org/10.5194/isprsarchives-XL-5-W4-171-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W4, 171-178, 2015
https://doi.org/10.5194/isprsarchives-XL-5-W4-171-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  18 Feb 2015

18 Feb 2015

AUTOMATIC ANALYSIS AND CLASSIFICATION OF THE ROOF SURFACES FOR THE INSTALLATION OF SOLAR PANELS USING A MULTI-DATA SOURCE AND MULTI-SENSOR AERIAL PLATFORM

L. López1, S. Lagüela2,1, I. Picon1, and D. González-Aguilera1 L. López et al.
  • 1Department of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50. 05003 Ávila, Spain
  • 2Applied Geotechnologies Research Group, University of Vigo. Rúa Maxwell s/n, Campus Lagoas-Marcosende, 36310 Vigo, Spain

Keywords: 3D reconstruction, aerial trike, photogrammetry, infrared thermography, point cloud, buildings, solar influence, solar panel

Abstract. A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbour solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the surfaces, slopes, orientations and the existence of obstacles. This way, large areas may be efficiently analysed obtaining as final result the optimal locations for the placement of solar panels as well as the required geometry of the supports for the installation of the panels in those roofs where geometry is not optimal.