This RoofArea Detection focuses on automating the detection of usable rooftop areas for solar panel installation using deep learning and image processing techniques. By analyzing satellite imagery, the system identifies rooftops, calculates their exact area, detects obstacles such as water tanks and AC units, and determines the roof's orientation to measure the azimuth angle. The goal is to significantly reduce the time and cost involved in manual field inspections while providing accurate and scalable assessments for solar energy deployment.

The Roofarea Detection addresses key challenges such as variations in roof colors, shapes, and the limitations of satellite image resolution, which often make it difficult to distinguish rooftops and identify small obstructions. These challenges are overcome using state-of-the-art deep learning algorithms, such as U-Net or Mask R-CNN, coupled with advanced image processing techniques. This combination enables the precise extraction of usable roof area and direction, contributing to efficient and optimized solar panel installations.