Advances in Deep Learning Models for Ground Deformation Prediction Using InSAR Approach: A Systematic Review with...

Ground deformation, especially subsidence, is a geo-environmental hazard that needs to be assessed and predicted in advance to mitigate its effects efficiently and in a timely manner. Interferometric Synthetic Aperture Radar (InSAR) provides high-resolution, all-weather ground deformation monitoring, while advances in Deep Learning (DL) have significantly improved its analytical capabilities. Despite growing interest in integrating DL with InSAR, existing systematic reviews have focused mainly on urban infrastructure, leaving mining-induced subsidence comparatively underexplored. This review addresses this gap by applying the PRISMA framework to systematically evaluate 162 peer-reviewed studies, including 21 core articles on DL-based mining subsidence and 58 supporting studies from related deformation applications. This paper examines DL architectures, including CNNs, LSTMs, GRUs, ConvLSTMs, Transformers, and Graph Neural Networks, and assesses their roles in data decomposition, spatiotemporal feature extraction, hyperparameter optimization, and multi-source data fusion. Comparative analysis of 71 models shows that attention-enhanced CNN-LSTM and ConvLSTM architectures consistently outperform single-model approaches. Key challenges include limited mining datasets, poor cross-site transferability, lack of standardized benchmarks, and insufficient geomechanical integration. Finally, we propose a seven-point research roadmap to support reliable, explainable, transferable, and operational early-warning systems for mining-induced subsidence monitoring.