Version 1
: Received: 14 February 2024 / Approved: 14 February 2024 / Online: 14 February 2024 (16:32:01 CET)
How to cite:
Lekavičius, J.; Gružauskas, V. Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images. Preprints2024, 2024020821. https://doi.org/10.20944/preprints202402.0821.v1
Lekavičius, J.; Gružauskas, V. Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images. Preprints 2024, 2024020821. https://doi.org/10.20944/preprints202402.0821.v1
Lekavičius, J.; Gružauskas, V. Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images. Preprints2024, 2024020821. https://doi.org/10.20944/preprints202402.0821.v1
APA Style
Lekavičius, J., & Gružauskas, V. (2024). Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images. Preprints. https://doi.org/10.20944/preprints202402.0821.v1
Chicago/Turabian Style
Lekavičius, J. and Valentas Gružauskas. 2024 "Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images" Preprints. https://doi.org/10.20944/preprints202402.0821.v1
Abstract
With the popularity of solar energy in the electricity market, demand arises for data such as precise locations of solar panels for efficient energy planning, management, and distribution. However, this data is not easily accessible and in some cases, information such as precise locations does not exist. Furthermore, existing data sets for training semantic segmentation models of 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 utilized, enriching the original resampled training data of varying GSDs without compromising its integrity. Experiments done with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to find the optimal configuration for an accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and utilizing an optimal amount – 60% of generated RS imagery for additional training data, increasing model accuracy. The findings demonstrate the benefits of using GAN-generated images as additional training data, increasing the size of small data sets, and improving the capabilities of the segmentation model for solar panel detection in RS images.
Keywords
deep learning; solar panels; semantic segmentation; data augmentation; generative adversarial network; remote sensing; transfer learning
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
