Potential of synthetic images in landslide segmentation in data-poor scenario: a framework combining GAN and transformer models

Abstract

Accurate landslide segmentation from remote sensing data is pivotal for efficient emergency response and risk management. In recent years, data-driven deep learning approaches have emerged as a significant area of focus in this domain. However, the limited availability of landslide data often restricts the effectiveness of these approaches. This study introduces the StyleGAN2-transformer framework for landslide segmentation, utilizing generative adversarial networks (GANs) for the first time to create synthetic, high-quality landslide images to address the data scarcity issue that undermines landslide segmentation model performance. Two datasets were developed: one containing a limited set of real landslide images and the other supplemented with synthetic landslide images generated by StyleGAN2. These datasets facilitated comparative experiments to quantitatively assess the impact of synthetic data on the performance of both convolutional neural network (CNN) and transformer series models, employing a suite of metrics for thorough evaluation. The findings indicate that adding synthetic landslide images from StyleGAN2 improves the overall accuracy of most landslide segmentation models significantly, achieving more than a 10% increase. Moreover, integrating StyleGAN2 with transformer models presents an optimized approach, as transformer models surpass CNN models in accuracy when adequate training data are available. Finally, the results also confirm that the StyleGAN2-transformer framework exhibits strong generalizability in a variety of scenarios.