用自监督方法实时监测环境灾害，无需标注数据且能区分季节变化。
SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping
- 基于轻量级条件UNet生成每日预期影像，建模正常季节变化。
- 在4个数据集上F1提升0.066~0.234，召回率更高且仅用473K参数。
- 适合跨区域、多类型灾害的快速部署，尤其适合缺乏标注数据场景。
气候变化导致环境灾害频发，亟需高效监测系统。现有方法或依赖昂贵标注数据，或受季节变化干扰，或需多时相确认（延迟检测）。本文提出SHAZAM——自监督变化监测框架，采用轻量级条件UNet生成感兴趣区域任意日期的预期影像，直接建模正常季节变化，实现潜在灾害区分。通过改进的结构相似性度量，将生成影像与实际卫星观测对比，计算区域异常分数与像素级灾害图。理论支撑的季节阈值避免了数据集特异性调参。在包含野火、烧毁区、异常降雪、洪水、干旱、赤潮和砍伐的四个多样化数据集上，SHAZAM的F1得分相比现有方法提升0.066至0.234。主要得益于更高召回率，且仅使用473K参数。其映射能力更优，空间分辨率更高，可有效抑制背景特征，同时突出即时与渐进式灾害。SHAZAM已验证为跨地理区域与多样灾害的通用高效解决方案。代码开源：https://github.com/WiseGamgee/SHAZAM
原文摘要 · Abstract (English)
The increasing frequency of environmental hazards due to climate change underscores the urgent need for effective monitoring systems. Current approaches either rely on expensive labelled datasets, struggle with seasonal variations, or require multiple observations for confirmation (which delays detection). To address these challenges, this work presents SHAZAM - Self-Supervised Change Monitoring for Hazard Detection and Mapping. SHAZAM uses a lightweight conditional UNet to generate expected images of a region of interest (ROI) for any day of the year, allowing for the direct modelling of normal seasonal changes and the ability to distinguish potential hazards. A modified structural similarity measure compares the generated images with actual satellite observations to compute region-level anomaly scores and pixel-level hazard maps. Additionally, a theoretically grounded seasonal threshold eliminates the need for dataset-specific optimisation. Evaluated on four diverse datasets that contain bushfires (wildfires), burned regions, extreme and out-of-season snowfall, floods, droughts, algal blooms, and deforestation, SHAZAM achieved F1 score improvements of between 0.066 and 0.234 over existing methods. This was achieved primarily through more effective hazard detection (higher recall) while using only 473K parameters. SHAZAM demonstrated superior mapping capabilities through higher spatial resolution and improved ability to suppress background features while accentuating both immediate and gradual hazards. SHAZAM has been established as an effective and generalisable solution for hazard detection and mapping across different geographical regions and a diverse range of hazards. The Python code is available at: https://github.com/WiseGamgee/SHAZAM
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