发表机构
Lund University（隆德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究揭示冻结地球观测嵌入的局部验证无法预测跨区域性能，但区域内可预测野火，提出评估应报告留出区域。
AI 中文摘要
冻结的地球观测嵌入几乎完全通过单一研究区域内的空间分块交叉验证来评估。我们证明，这一数值无法预测在新区域中的准确性；我们展示了原因；并展示了一种该模型确实保持有效的设置，使用仅需线性探针和已有标签的协议。测试平台是野火，使用哥白尼计划中希腊和西班牙六场火灾的烧毁面积图以及每场火灾前一年的描述符，比较TESSERA和AlphaEarth与ESA WorldCover类别及年度Sentinel-2指数摘要。在火灾内部，嵌入识别烧毁土地的ROC AUC比指数摘要高0.05至0.13，重复的折分配、空间缓冲区、块自助法和梯度提升树均未改变这一差距。在另一区域的火灾上，嵌入损失0.15至0.18的AUC，而指数摘要损失0.06，因此三者最终相差几个百分点。表征并非原因。来自新区域的八个带标签块恢复了嵌入优势，并比来自其他区域的59,000个带标签像素获得更高的AUC，且在一个区域拟合的权重向量与在其他区域拟合的向量几乎正交，因此跨区域携带的部分小而低维。区域内预测则是另一回事。在2023年燃烧的火灾上拟合，并应用于十二公里外2024年燃烧的火灾（其中没有任何数据晚于目标火灾），TESSERA达到0.772的AUC，相比在2024年火灾内部拟合的分类器损失0.04，而在其他区域拟合的分类器损失0.09至0.18。因此，拥有一个已映射火灾的区域可以预测该区域后续火灾的易感性；没有火灾的区域无法从其他地方借用模型，且每次对冻结嵌入的评估都应报告一个留出区域。
英文摘要
Frozen Earth-observation embeddings are judged almost entirely by spatially blocked cross-validation inside one study region. We show that this number does not predict accuracy in a new region; we show why; and we show the one setting in which such a model does keep working, using a protocol that needs only a linear probe and labels one already has. The testbed is wildfire, with Copernicus burned-area maps of six fires in Greece and Spain and descriptors from the year before each fire, comparing TESSERA and AlphaEarth with ESA WorldCover classes and annual Sentinel-2 index summaries. Inside a fire, the embeddings identify the burned land 0.05 to 0.13 ROC AUC better than the index summaries, and repeated fold allocations, spatial buffers, a block bootstrap, and gradient-boosted trees leave that margin unchanged. On a fire in another region, they lose 0.15 to 0.18 AUC, and the index summaries lose 0.06, so the three end within a few hundredths of each other. The representation is not the cause. Eight labelled blocks from the new region restore the embedding advantage and give a higher AUC than 59,000 labelled pixels from other regions, and the weight vector fitted in one region is nearly orthogonal to the vector fitted in the others, so the part that carries across regions is small and low-dimensional. Forecasting within a region is a different matter. Fitted on a fire that burned in 2023 and applied to a fire twelve kilometres away that burned in 2024, where nothing used postdates the target fire, TESSERA reaches 0.772 AUC and loses 0.04 against a classifier fitted inside the 2024 fire, while classifiers fitted in other regions lose 0.09 to 0.18. A region with one mapped fire can therefore forecast susceptibility for later fires there; a region without one cannot borrow a model from elsewhere, and every evaluation of a frozen embedding should report a held-out region.
Comments18 pages, 5 figures, 8 tables
