Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images

Abstract

With the popularity of solar energy in the electricity market, demand rises for data such as precise locations of solar panels for efficient energy planning and management. However, these data are not easily accessible; information such as precise locations sometimes does not exist. Furthermore, existing datasets for training semantic segmentation models of photovoltaic (PV) installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is used, enriching the original resampled training data of varying ground sampling distances (GSDs) without compromising their integrity. Experiments with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to discover the advantage of using GAN-based data augmentations for a more accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and an optimal amount—60% of GAN-generated RS imagery for additional training data. The findings demonstrate the benefits of using GAN-generated images as additional training data, addressing the issue of limited datasets, and increasing IoU and F1 metrics by 2% and 1.46%, respectively, compared with classic augmentations.