Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis; (2017) Building rooftop classification using random forests for large-scale PV deployment. In: Michel, Ulrich and Schulz, Karsten and Nikolakopoulos, Konstantinos G and Civco, Daniel, (eds.) Proceedings of SPIE Earth Resources and Environmental Remote Sensing/GIS Applications VIII. (pp. 1042806-1-1042806-12). SPIE: Bellingham, WA, USA.

Preview

Text SPIE 1042806.pdf - Published Version Download (2MB) | Preview

Abstract

Large scale solar Photovoltaic (PV) deployment on existing building rooftops has proven to be one of the most efficient and viable sources of renewable energy in urban areas. As it usually requires a potential analysis over the area of interest, a crucial step is to estimate the geometric characteristics of the building rooftops. In this paper, we introduce a multi-layer machine learning methodology to classify 6 roof types, 9 aspect (azimuth) classes and 5 slope (tilt) classes for all building rooftops in Switzerland, using GIS processing. We train Random Forests (RF), an ensemble learning algorithm, to build the classifiers. We use (2 × 2) [m2 ] LiDAR data (considering buildings and vegetation) to extract several rooftop features, and a generalised footprint polygon data to localize buildings. The roof classifier is trained and tested with 1252 labeled roofs from three different urban areas, namely Baden, Luzern, and Winterthur. The results for roof type classification show an average accuracy of 67%. The aspect and slope classifiers are trained and tested with 11449 labeled roofs in the Zurich periphery area. The results for aspect and slope classification show different accuracies depending on the classes: while some classes are well identified, other under-represented classes remain challenging to detect.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item