提出TARDIS方法，实现遥感图像中分布偏移的高效检测。
Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
- 基于特征空间内激活值生成替代标签，无需真实异常数据
- 在17组实验中13次接近最优标签性能，优于主流方法
- 适用于大规模遥感模型部署，可揭示模型实际表现
训练鲁棒的深度学习模型对地球观测至关重要，因全球部署模型常面临分布偏移问题，尤其在数据稀缺区域性能下降明显。为应对这一挑战，本文提出一种后处理式无监督分布外（OOD）检测方法TARDIS，专为地理空间场景规模化部署设计。其核心思想是利用模型内部特征空间中的正常数据（ID）生成替代分布标签。TARDIS接收预训练模型、正常数据及未知分布数据（WILD），依据内部激活值将WILD划分为伪正常与伪异常两类，并训练二分类器以识别分布偏移。我们在EuroSAT和xBD数据集上进行了17组实验，涵盖协变量与语义偏移，结果表明：13次实验中替代标签性能接近理论上限，且在性能上可媲美顶尖激活值与评分类后处理方法。最后，通过在Fields of the World数据集上的部署，揭示了预训练模型在真实世界中的行为模式。代码已开源。
原文摘要 · Abstract (English)
Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID) data. However, existing methods either assume access to OOD data or compromise primary task performance, limiting real-world use. We introduce TARDIS, a post-hoc OOD detection method designed for scalable geospatial deployment. Our core innovation lies in generating surrogate distribution labels by leveraging ID data within the feature space. TARDIS takes a pre-trained model, ID data, and data from an unknown distribution (WILD), separates WILD into surrogate ID and OOD labels based on internal activations, and trains a binary classifier to detect distribution shifts. We validate on EuroSAT and xBD across 17 setups covering covariate and semantic shifts, showing near-upper-bound surrogate labeling performance in 13 cases and matching the performance of top post-hoc activation- and scoring-based methods. Finally, deploying TARDIS on Fields of the World reveals actionable insights into pre-trained model behavior at scale. The code is available at \href{https://github.com/microsoft/geospatial-ood-detection}{https://github.com/microsoft/geospatial-ood-detection}
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