solar_detection | Automatic Boundary Extraction of Large-Scale Photovoltaic

solar_detection is a Jupyter Notebook library. solar_detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This study presents a novel method for boundary extraction of Photovoltaic (PV) plants using a Fully Convolutional Network (FCN). Extracting the boundaries of PV plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. The presented deep learning based 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. For this purpose, as a prerequisite, the “Amir” dataset consisting of aerial images of PV plants from different countries, has been collected. A Mask RCNN architecture is employed as a deep network with customized VGG16 as encoder to detect the boundaries precisely. As comparison, the results of another framework based on Classical Image Processing (CIP) are compared with the proposed encoder-decoder network's performance in PV plants boundary detection. The results of the this fully convolutional network demonstrate that the trained model is able to detect the boundaries of PV plants with an accuracy of 96.99% and site-specific tuning of boundary parameters is no longer required.