地球表面免疫系统：用于未知异常快速监测
Earth Surface Immune System for Rapid Monitoring of Unknown Anomalies
- Wuhan University（武汉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出地球表面免疫系统ESIA，受生物免疫原理启发，通过先天与适应性阶段及突变机制，实现未知异常的快速定位与开放词汇识别，在多个数据集上超越现有方法，助力灾害响应。
AI中文摘要：
由日益加剧的气候变化和不断扩张的人类活动所驱动的地球表面异常，其发生频率和多样性均在增加，然而其有限的历史数据和不可预测性使其从根本上区别于传统的遥感目标。现有方法要么针对特定异常类别，要么止步于定位，在检测与可操作信息之间留下了空白。在此，我们提出ESIA——一种地球表面免疫系统，其架构受生物免疫系统三条原则约束，该系统经过数百万年进化以应对同样多样且不确定的威胁。非特异性先天免疫阶段将异常视为时间序列卫星影像中未观测到的变化，在不假设任何异常类别的情况下，以14.51平方公里/秒的速度生成二值定位图，在F1分数上超越最强通用基线37%。特异性适应性免疫阶段应用负选择来过滤文本提示，并通过多模态基础模型将存活的提示与定位的图像块进行匹配，实现对未知异常属性（包括类别、受影响面积和损害严重程度）的开放词汇识别，识别F1分数超过80%。突变机制在测试时调整最小嵌入，利用单个参考图像对在3.26秒内适应每个场景。我们在覆盖六个异常类别、总面积达19,801.60平方公里的全球尺度数据集上验证了ESIA，并与22个模型进行比较，还将其应用于量化卡霍夫卡大坝坍塌后第聂伯河三角洲的退化农田，以及评估2025年洛杉矶帕利塞兹火灾的烧伤严重程度。这种处理未知异常的前所未有的灵活性，为实时灾害响应和环境监测开辟了新途径。
英文摘要：
Earth surface anomalies, driven by escalating climate change, and expanding human activities, are increasing in both frequency and diversity, yet their limited historical data and unpredictability make them fundamentally different from conventional remote sensing targets. Existing methods address specific anomaly categories or stop at localization, leaving a gap between detection and actionable information. Here we present ESIA, an Earth Surface Immune System whose architecture is constrained by three principles from the biological immune system, refined over millions of years against equally diverse and uncertain threats. A non-specific innate immune stage treats anomalies as unobserved changes in time-series satellite imagery, generating binary localization maps at 14.51 km2/s without assuming any anomaly category, surpassing the strongest general baseline by 37% in F1. A specific adaptive immune stage applies negative selection to filter text prompts and matches surviving prompts with localized image patches through a multi-modal foundation model, enabling open-vocabulary recognition of unknown anomaly attributes including category, affected area, and damage severity, with recognition F1 exceeding 80%. A mutation mechanism tunes minimal embeddings at test time, adapting to each scene in 3.26s using a single reference image pair. We validate ESIA on a global-scale dataset covering 19,801.60 km2 across six anomaly categories, comparing against 22 models, and further apply it to quantify degraded farmland in the Dnipro Delta following the Kakhovka Dam collapse and assess burn severity from 2025 Palisades Fire in Los Angeles. This unprecedented flexibility in handling unknown anomalies opens new avenues for real-time disaster response and environmental surveillance.
