Synthetic Data Augmentation with Generative Models for Improved Classification of Mine Tailings Impoundments

Classifying mine tailings impoundments is challenging due to the complexity and variability of these landscapes. A major hurdle is the scarcity of annotated real-world images, leading to issues such as underfitting and class imbalance in machine learning models. To address these challenges, we incorporate synthetic data during training to improve classification accuracy. This research aims to boost the accuracy and generalization of the model using real-world Sentinel-1 and Sentinel-2 datasets and synthetic images generated by generative models. We evaluated several image generation techniques, such as Conditional Generative Adversarial Networks, Variational Autoencoders, PixelCNN and Diffusion Models. These methods are assessed for their ability to produce realistic, high-quality synthetic images to augment datasets and thus improve pattern recognition tasks. In addition, we can accurately capture the structure of mine tailings landscapes by integrating amplitude information from synthetic aperture radar and multispectral optical data. Through our analysis of these approaches, we aim to reduce the reliance on large labeled datasets, mitigate mislabeling, and enhance both the accuracy and generalization of classification models. This work contributes to more effective monitoring of mine tailings impoundments, ultimately supporting better environmental risk assessments and the advancement of sustainable mining practices.