Open AccessQuantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks
Author(s) -
R. Castello,
Alina Walch,
Raphaël Attias,
Riccardo Cadei,
Shasha Jiang,
Jean-Louis Scartezzini
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2042/1/012002
Subject(s) - convolutional neural network , geospatial analysis , leverage (statistics) , computer science , exploit , roof , overhead (engineering) , artificial intelligence , intersection (aeronautics) , artificial neural network , remote sensing , engineering , cartography , civil engineering , geography , operating system , computer security
The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.