Inferring roof semantics for more accurate solar potential assessment

Student report (2021)

Authors

I.M.M. Apra Architecture and the Built Environment
C. Bachert Architecture and the Built Environment
C.A. Caceres Tocora Architecture and the Built Environment
ÖZGE TUFAN Architecture and the Built Environment
O. Veselý Architecture and the Built Environment

Contributors

E. Verbree GIS Technologie - Architecture and the Built Environment (supervisor 1)
Faculty
Architecture and the Built Environment
Copyright: © 2021 Irène Apra, Carolin Bachert, Camilo Caceres Tocora, ÖZGE TUFAN, Ondrej Veselý
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Published Date

30-06-2021

Language

English

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Faculty

Architecture and the Built Environment

Abstract

Led in cooperation with the company Brink, who provides management and consultation services for construction and real estate sectors, this Synthesis Project aims at automatically deriving meaningful information about buildings. More precisely, the focus is to automatically detect roof obstacles - such as dormers, chimneys, and solar panels - to be able to determine the available roof surface for new solar panel installation, and therefore to perform more accurate solar potential analysis. For this purpose, three different methods are developed and implemented to increase the results’ accuracy, which are geometry-based, unsupervised, and supervised classification. While AHN3 point cloud and 3D BAG Level of Detail (LoD) 2.2 building models are used for the geometry-based classification, the input data of the unsupervised image classification consists of aerial images and BAG footprints. Finally, supervised image classification method makes use of the aerial images as well as the BAG footprints and a dataset of manually labelled solar panel polygons. The results show that the accuracy of individual methods is not sufficient; therefore, the outputs of all three methods are merged together into one pipeline, with the aim of obtaining one final end product. The latter is the 3D BAG LoD2.2 building model in CityJSON format, enhanced with three new attributes per building: the obstacle area on the roof, the available area for installing solar panels, and a Boolean value showing whether the building has existing solar panels or not. Additionally, an enhanced point cloud for future use is generated, with a new attribute per point indicating its distance to the 3D model and therefore its potential for being an obstacle or not. The assessment of the results with the ground truth illustrates that the algorithm gives promising results; however, the scope of the project can be broadened, and improvements can be made to increase the accuracy as well as the efficiency.