Geospatial AI
Climate Anomaly Detection from Satellite Time Series
Published: 2025
A self-supervised transformer framework for detecting long-term climate anomalies from satellite time-series imagery.
Problem Statement
Conventional anomaly detection models struggle with seasonal variations and long temporal dependencies in satellite datasets.
Methodology
Applied self-supervised pretraining on satellite time-series data followed by anomaly scoring using attention-based reconstruction loss.
Technology Stack
Self-Supervised LearningTransformersMulti-Spectral Satellite ProcessingTime-Series ModelingGeospatial Data Fusion
Results & Impact
Detected early vegetation stress patterns 6 months before visible ecological degradation occurred.
Future Work
Combining anomaly detection with wildlife migration modeling for ecosystem-level intelligence.