SOLAR PANEL MAPPING VIA ORIENTED OBJECT DETECTION (Papers Track)
Conor Wallace (DroneBase); Isaac Corley (University of Texas at San Antonio); Jonathan Lwowski (DroneBase)
Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a de- tailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this task is extremely tedious and is not scalable for the ever increasing capacity of so- lar power across the globe. Therefore, we propose an end-to-end deep learning framework for detecting individual solar panels using a rotated object detection architecture. We evaluate our approach on a diverse dataset of solar power plants collected from across the United States and report a mAP score of 83.3%.