Monitoring mining-induced subsidence from satellite imagery using transformer-based deep learning trained on gridded...

The inherent concealment of underground coal mining makes it difficult for environmental protection authorities to detect and regulate illicit activities. These mining activities are only identified after severe environmental damage has occurred, such as farmland flooding or structural cracks in residential buildings. By the time enforcement actions are taken, the opportunity for early intervention is lost, and ecological restoration becomes nearly impossible. Using artificial intelligence (AI) to analyse satellite imagery for monitoring land subsidence in coal mining-affected areas is considered a promising solution. However, two major research gaps remain unresolved. First, the lack of ground-truth subsidence measurements limits the amount of training data available for AI models. Second, traditional convolutional neural network (CNN) architectures, such as VGGNet and ResNet, often fail to achieve satisfactory classification accuracy in this context. In this study, a Vision Transformer (ViT-Base) model was trained using 191,630 land subsidence grid measurements paired with high-resolution satellite images. The model achieved an overall accuracy of 94 % in identifying land subsidence in the region corresponding to the training data. To further evaluate its generalizability, ten representative mining-affected areas were selected from China's top ten coal-producing provinces, each providing 250 independent subsidence grid measurements paired with high-resolution satellite imagery. The overall accuracies obtained were ranging from 77.2 % to 84.8 %. These results demonstrate that ViT-Base consistently outperforms conventional models in identifying mining-induced land subsidence from satellite imagery, maintaining high accuracy across diverse geographic and geological settings while requiring less training data. The proposed model thus addresses key research gaps and provides a practical tool for the monitoring and management of mining-induced land subsidence.