Learning InSAR Deformation Representations

The detection and characterization of ground deformations play a critical role in understanding and mitigating the risks associated with natural hazards such as earthquakes, volcanic activity, and land subsidence. These deformations can have significant impacts on human life, infrastructure, and the environment, making their timely and accurate detection essential for disaster management and planning. Remote sensing technologies, particularly those that offer global coverage and high temporal resolution, are indispensable in capturing ground motion over large areas. 
Synthetic Aperture Radar (SAR) data, particularly from the Sentinel-1 mission, has revolutionized geodesy and remote sensing by providing high-resolution and frequent observations of Earth's surface. Interferometric SAR (InSAR) techniques allow for precise measurement of surface displacements at millimeter-scale accuracy, enabling the detection of subtle ground deformation patterns. Despite the availability of massive datasets from Sentinel-1, most of this data remains unlabeled, limiting the ability to directly apply supervised machine learning techniques for deformation classification. The need to manually label vast amounts of data is both time-consuming and resource-intensive, leaving a significant portion of the data underutilized for scientific discovery and practical applications.
Representation learning offers a promising approach to address these challenges by extracting meaningful features from large, unlabeled InSAR datasets. Self-supervised learning methods can leverage contrastive learning techniques to pretrain neural network encoders on deformation data, capturing patterns and structures inherent in the data without requiring labels. These learned representations can then be fine-tuned for downstream tasks, such as classifying deformation types (e.g., magma movements during volcanic eruptions or ground deformations coming from earthquakes) or detecting anomalies. By bridging the gap between vast, unlabeled data and the need for precise classification, representation learning enables more efficient use of InSAR datasets, advancing our ability to monitor and understand Earth's dynamic processes.