Spatiotemporal Prediction of Urban Land Subsidence Using ConvLSTM Enhanced with Spatial Attention Mechanism

Rapid urbanization has increasingly posed risks of inducing land subsidence in newly developed urban districts, posing growing threats to infrastructure safety. This study focuses on a selected rectangular area within the Shannan New District of Huainan City. Based on 94 Sentinel-1A images acquired from 2023 to 2025, the SBAS-InSAR technique was employed to obtain high-density spatiotemporal surface deformation data. The discrete monitoring points were mapped onto a 100 × 100 regular grid according to their spatial coordinates, with null-value cells retained. A spatial attention mechanism was then embedded into the Convolutional Long Short-Term Memory (ConvLSTM) network to construct a Spatial Attention–ConvLSTM (SA-ConvLSTM) model for spatiotemporal prediction, which was systematically compared with LSTM, CNN-LSTM (Convolutional Neural Network combined with Long Short-Term Memory), and standard ConvLSTM. The results demonstrate that SA-ConvLSTM achieves optimal prediction performance on the temporal hold-out test set, with a root mean square error of 2.09 mm and a coefficient of determination (R2) of 0.77. For subsidence hotspot identification, the intersection over union (IoU) reaches 0.56, and the F1-score reaches 0.72—substantially improving from 0.25 for standard ConvLSTM, confirming that the spatial attention mechanism effectively enhances the model’s capability to focus on key deformation areas. Rolling predictions of the deformation field for 2026 (12 time steps, each covering one Sentinel-1A acquisition interval of approximately 12 days) yield an estimated deformation trend ranging from −16.58 to 0.28 mm over the 12-step forecast period (approximately 144 days). This integrated framework provides a methodological reference for subsidence risk identification and mitigation in the Shannan New District.