A Deep Learning Approach for Spatio-Temporal Forecasting of InSAR Ground Deformation in Eastern Ireland

• URL: http://arxiv.org/abs/2509.18176v1
• Date: Wed, 17 Sep 2025 17:10:18 GMT
• Title: A Deep Learning Approach for Spatio-Temporal Forecasting of InSAR Ground Deformation in Eastern Ireland
• Authors: Wendong Yao, Saeed Azadnejad, Binhua Huang, Shane Donohue, Soumyabrata Dev,
• Abstract summary: Monitoring ground displacement is crucial for urban infrastructure and mitigating geological hazards.<n>This paper introduces a novel deep learning framework that transforms sparse point measurements into a dense-temporal tensor.<n>Results demonstrate that the proposed architecture provides more accurate and spatially coherent forecasts.
• Score: 2.840858735842673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monitoring ground displacement is crucial for urban infrastructure stability and mitigating geological hazards. However, forecasting future deformation from sparse Interferometric Synthetic Aperture Radar (InSAR) time-series data remains a significant challenge. This paper introduces a novel deep learning framework that transforms these sparse point measurements into a dense spatio-temporal tensor. This methodological shift allows, for the first time, the direct application of advanced computer vision architectures to this forecasting problem. We design and implement a hybrid Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) model, specifically engineered to simultaneously learn spatial patterns and temporal dependencies from the generated data tensor. The model's performance is benchmarked against powerful machine learning baselines, Light Gradient Boosting Machine and LASSO regression, using Sentinel-1 data from eastern Ireland. Results demonstrate that the proposed architecture provides significantly more accurate and spatially coherent forecasts, establishing a new performance benchmark for this task. Furthermore, an interpretability analysis reveals that baseline models often default to simplistic persistence patterns, highlighting the necessity of our integrated spatio-temporal approach to capture the complex dynamics of ground deformation. Our findings confirm the efficacy and potential of spatio-temporal deep learning for high-resolution deformation forecasting.

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