Identify Solar Panels in Aerial Imagery with Computer Vision

Computer vision can be effectively utilized to identify solar panels in aerial imagery, offering various applications such as government analysis for solar panel distribution and insurance verification. This guide demonstrates the process using a pre-trained model available on Roboflow Universe, which achieves a 70% mean average precision score and can be tested or deployed via the Roboflow API or on personal hardware. Users have the option to enhance the model with their data and integrate additional functionalities, like identifying roofs for more precise solar panel counting. The guide also illustrates how to visualize model predictions using the Python package, Supervision, and suggests potential applications for the model, such as monitoring solar panel trends in urban areas.