Building rooftop classification using random forests for large-scale PV deployment

Dan Assouline; Nahid Mohajeri; Jean-Louis Scartezzini

doi:10.1117/12.2277692

5 October 2017 Building rooftop classification using random forests for large-scale PV deployment

Dan Assouline, Nahid Mohajeri, Jean-Louis Scartezzini

Proceedings Volume 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII; 1042806 (2017) https://doi.org/10.1117/12.2277692
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

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.

Conference Presentation

Citation Download Citation

Dan Assouline, Nahid Mohajeri, and Jean-Louis Scartezzini "Building rooftop classification using random forests for large-scale PV deployment", Proc. SPIE 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 1042806 (5 October 2017); https://doi.org/10.1117/12.2277692

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