Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data

TL;DR

This paper introduces a comprehensive method combining computer vision, structured data, and geometric analysis to accurately estimate the solar energy potential of rooftops without extensive on-site measurements.

Contribution

It presents a novel integrated approach using image segmentation and structured data to predict rooftop solar potential efficiently and accurately.

Findings

Effective semantic segmentation of roof features achieved

Accurate prediction of roof pitch and solar panel placement

Reliable estimation of yearly solar potential from imagery and data

Abstract

Estimating the amount of electricity that can be produced by rooftop photovoltaic systems is a time-consuming process that requires on-site measurements, a difficult task to achieve on a large scale. In this paper, we present an approach to estimate the solar potential of rooftops based on their location and architectural characteristics, as well as the amount of solar radiation they receive annually. Our technique uses computer vision to achieve semantic segmentation of roof sections and roof objects on the one hand, and a machine learning model based on structured building features to predict roof pitch on the other hand. We then compute the azimuth and maximum number of solar panels that can be installed on a rooftop with geometric approaches. Finally, we compute precise shading masks and combine them with solar irradiation data that enables us to estimate the yearly solar potential of a rooftop.