基于嵌入的异常检测用于清理全球作物类型参考数据集
Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets
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中文总结 AI 辅助
研究通过地理空间基础模型生成的嵌入能否用于清理全球作物类型参考数据集，提出基于嵌入的局部感知异常检测框架，并通过实验验证其有效性，改进了作物类型模型，为清理地球观测参考数据集提供了可复制扩展的模板。
中文摘要 AI 辅助
高质量参考数据是作物类型映射在任何时空尺度上的关键瓶颈。像WorldCereal这样的操作系统聚合来自不同来源的标签，存在偏差、覆盖差距和未知标签噪声。本文聚焦通过地理空间基础模型生成的嵌入能否作为清理参考数据的可行基础这一问题。提出基于嵌入的局部感知异常（EBA）检测框架，用预训练嵌入对标记样本评分，标记突出样本并测试处理后对模型的影响。通过两种方式确定标记点确实错误标记或位置不当，按标记处理后改进了WorldCereal作物类型模型，发现保守清理有益过度清理有害，该方法可复制扩展，能为清理地球观测参考数据集提供模板。
英文摘要
High quality reference data remain a critical bottleneck for crop-type mapping at any spatial and temporal scale. Operational systems such as WorldCereal aggregate labels from heterogeneous sources such as parcel registers, national databases, field surveys, and map-derived products, each with their own biases, coverage gaps and unknown label noise. Simple global rules are inadequate, since crop phenology and observation conditions vary strongly across regions and seasons. In this study, we focus on a single, operationally relevant question: whether embeddings produced through geospatial foundation models are a viable basis for cleaning the reference data. We propose a practical, locality-aware, embedding-based anomaly (EBA) detection framework that operates on the embeddings of a pretrained Earth-observation encoder. We score each labelled sample against other samples of the same crop in the same area using a pretrained embedding, flag the ones that stand out, and test whether removing or down-weighting them before training yields a better model. We establish that the flagged points are genuinely mislabelled or misplaced in two independent ways: against synthetic ground truth, the detector concentrates injected label errors 2.5-5x above chance in its flagged set (detection AUROC up to 0.84); and on real data, a model-independent test shows that removing or confidence-weighting the flagged held-out points raises measured accuracy in trained models, for both crop type and land cover. Acting on the flags then improves the WorldCereal crop-type model across five macro-regions, evaluated on a fixed held-out split under three views. We find conservative cleaning helps while over-cleaning hurts. The EBA detector approach is designed to be reproducible and extensible, and can serve as a template for cleaning large, noisy Earth observation reference datasets beyond crop mapping.
发表机构
- VITO Remote Sensing（弗拉芒技术研究所遥感部）
- European Space Agency (ESA)（欧洲航天局）
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