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Self-Supervised Temporal Pattern Mining for coastal climate resilience planning for extreme data sparsity scenarios

Self-Supervised Temporal Pattern Mining for coastal climate resilience planning for extreme data sparsity scenarios

Self-Supervised Temporal Pattern Mining for coastal climate resilience planning for extreme data sparsity scenarios Introduction: A Lesson from the Buoy My journey into this specific niche began during a late-night debugging session while working on a time-series forecasting model for a local environmental NGO. We were trying to predict storm surge impacts on a vulnerable coastline in the Global South. The problem wasn't the complexity of the transformer architecture I was experimenting with; it was the data. We had a decade of satellite imagery, but the in-situ sensor data—the actual buoys measuring wave height and water levels—was sparse. We had gaps of months, sometimes years. I remember staring at the sparse time-series plot, realizing that traditional supervised learning was failing because we simply didn't have enough labels to train a robust model. This is the reality for 90% of coastal resilience projects in developing regions. We are drowning in raw satellite bits but starving for structured, labeled temporal data. This frustration led me down a rabbit hole of Self-Supervised Learning (SSL) and Temporal Pattern Mining. I wanted to know: can we extract meaningful climate signals from noisy, sparse data without manual labeling? While exploring the intersection of contrastive learning and chaotic dynamical systems, I discovered that the patterns hidden in the structure of the data itself—even when sparse—are often enough to reconstruct the missing physics. This article details my technical exploration into building a self-supervised pipeline for coastal resilience, specifically tailored for scenarios where data is a luxury we don't have. Coastal climate resilience planning relies on understanding three core variables: Sea Surface Temperature (SST), Sea Level Anomaly (SLA), and Significant Wave Height (SWH). In data-rich regions like the US East Coast, we have continuous streams from NOAA buoys. However, in the "Extreme Data Sparsity" (EDS) scenarios typical of

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