Maintaining the integrity of solar power plants is a vital component in dealingwith 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 possibleto quickly locate and mitigate potential faulty solar panels. However, this taskis 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 learningframework for detecting individual solar panels using a rotated object detectionarchitecture. We evaluate our approach on a diverse dataset of solar power plantscollected from across the United States and report a mAP score of 83.3%.