Inferring roof semantics for more accurate solar potential assessment
I.M.M. Apra (TU Delft - Architecture and the Built Environment)
C. Bachert (TU Delft - Architecture and the Built Environment)
C.A. Caceres Tocora (TU Delft - Architecture and the Built Environment)
ÖZGE TUFAN (TU Delft - Architecture and the Built Environment)
O. Veselý (TU Delft - Architecture and the Built Environment)
E Verbree (GIS Technologie)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.