用卫星影像时序数据提前发现极端天气，提升灾害响应效率
Leveraging Satellite Image Time Series for Accurate Extreme Event Detection
- 融合灾前多时相影像，分离灾害相关信号
- 在真实与合成数据上显著优于双时相基线方法
- 适用于多种灾害类型，适合大规模监测应用
气候变化正导致极端天气事件频发，造成重大环境破坏和人员伤亡。早期检测此类事件对提升灾后响应至关重要。本文提出 SITS-Extreme 框架，利用卫星影像时序数据，通过整合多个灾前观测，有效过滤无关变化，突出灾害相关信号，实现更精准的事件检测。在真实世界与合成数据集上的大量实验验证了该方法的有效性，相比广泛应用的强双时相基线方法有显著提升。此外，我们分析了增加时间步数的影响，评估了框架中关键组件的贡献，并在不同灾害类型下测试性能，为该方法在大规模灾害监测中的可扩展性和适用性提供了重要洞察。
原文摘要 · Abstract (English)
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.
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