Aerial Imagery of PV Plants for boundary detection

Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 

Preparing the data is the primary step for training a Fully Convolutional Network (FCN). Accordingly, the “Amir” dataset is developed for use in the autonomous monitoring of large-scale PV plants. This dataset includes a set of aerial imagery collected from different large-scale PV plants located in twelve countries from six continents. In the Amir dataset, all necessary data has been provided to train an accurate FCN model for boundary detection, including aerial imagery of PV plants and their masks. Amir covers many PV plants throughout the world. In addition, the aerial images are fully annotated to specify the boundaries precisely. Amir consists of 3580 aerial images from mainly large-scale PV plants. The Amir dataset has been developed for a research project entitled  " smart and autonomous monitoring system for large-scale PV Plants''. The detail information about this dataset and its applications can be found in the paper entitled ''Automatic Boundary Extraction of Large-Scale Photovoltaic Plants using a Fully Convolutional Network on Aerial Imagery".