Leveraging Satellite Image Time Series for Accurate Extreme Event Detection
This addresses early detection of climate-related disasters for improved disaster response, representing a domain-specific advancement with competitive performance.
The paper tackles the problem of detecting extreme weather events from satellite imagery by proposing SITS-Extreme, a framework that uses multiple pre-disaster observations in time series to filter out irrelevant changes and isolate disaster signals, resulting in substantial improvements over bi-temporal baselines.
Climate change is leading to an increase in extreme weather events, causing significant environmental damage and loss of life. Early detection of such events is essential for improving disaster response. In this work, we propose SITS-Extreme, a novel framework that leverages satellite image time series to detect extreme events by incorporating multiple pre-disaster observations. This approach effectively filters out irrelevant changes while isolating disaster-relevant signals, enabling more accurate detection. Extensive experiments on both real-world and synthetic datasets validate the effectiveness of SITS-Extreme, demonstrating substantial improvements over widely used strong bi-temporal baselines. Additionally, we examine the impact of incorporating more timesteps, analyze the contribution of key components in our framework, and evaluate its performance across different disaster types, offering valuable insights into its scalability and applicability for large-scale disaster monitoring.