Photovoltaic Power Station Extraction from High-Resolution Satellite Images based on Deep Learning Method

As an important part of the renewable energy, photovoltaic power generation industry has developed rapidly all around China in recent years, however some land use problems have also emerged. Therefore it is of great significance to monitor the number and distribution of photovoltaic power stations timely and accurately with high-resolution satellite images for the healthy development of photovoltaic industry. Combined with the improved DeepLab V3+ model and the ResNeSt-50 backbone network, the paper designs an effective photovoltaics extraction semantic segmentation algorithm and trains the new extraction model iteratively by making full use of big and various photovoltaic land samples. Photovoltaics are extracted accurately all over China with Chinese high-resolution satellite images, following a series of post-processing algorithms, such as binarizing, small and pseudo targets automatic removing, etc. Results show that the accuracy rate of photovoltaic land extraction is about 72.36% and the recall rate is about 91.06%. This precision is good enough for photovoltaic land extraction nationwide annually and the proposed deep learning model is efficient, small and can widely be used with other natural resources target extraction.