Roof Detection Algorithm

Our customer faced significant challenges in accurately assessing roofs for solar panel installation. Technicians were often sent to houses only to find that installations were not feasible due to roof obstructions or orientation, leading to a 20% rate of unnecessary visits. The existing process was not only inefficient but also costly, and there was a need for a solution that could automate roof assessments using satellite imagery, minimizing the risk and investment required for on-site evaluations.

We developed a computer vision solution using a U-Net Convolutional Network architecture, designed to tackle three key targets: roof detection, obstruction detection, and roof orientation identification (specifically, identifying south-facing roofs ideal for solar panels). Despite having only 60 tagged images to work with, we were able to create a highly effective proof of concept that demonstrated the model's generalization power.

Exceptional results were achieved through innovative solutions, turning the challenge at hand into measurable success.