Solar Roof Measurement with Computer Vision

The growing demand for solar panel installations has led to the need for accurate roof surface area measurements, which can now be efficiently automated using computer vision and high-resolution aerial imagery. The process involves training an instance segmentation model to detect roof areas in drone-captured images, using tools like Roboflow to annotate these areas with precise polygon coordinates. Once the roof areas are identified, the Shoelace algorithm is employed to calculate their surface area in pixels, which is then converted into real-world units using the Ground Sample Distance (GSD). This method ensures precise measurements necessary for solar panel planning, taking into account the camera's sensor size, focal length, flight altitude, and image dimensions to accurately map roof areas. Visualization of the results with Matplotlib allows for a clear representation of each roof's area, facilitating the estimation of solar panel installations by calculating the number of panels that can fit the available space. The accuracy of these measurements is crucial for optimizing energy production, cost estimation, and urban development, underscoring the importance of high-resolution images and precise camera calibration in achieving reliable results.