用少量已知遗址数据，从海量地理图像中精准定位未知遗址。
Needles in the Landscape: Semi-Supervised Pseudolabeling for Archaeological Site Discovery under Label Scarcity
- 设计双路伪标签方法，直接从多光谱影像学习遗址分布规律。
- 在塞加拉索斯数据集上召回率提升29%，F1提高12%。
- 适合遗址数据稀缺但需高效发现的考古探测任务。
考古预测建模通过结合已知遗址位置与环境地理变量，估算未知遗址的可能分布，面临正例稀少、多数样本无标签的正-未标记（PU）学习挑战。为应对这一问题，本文提出非对称双伪标签（DPL）方法，一种端到端深度学习框架，直接从多波段地理空间图像中学习，无需手工特征工程或对遗址缺失的假设。在两个重要考古数据集上进行评估：在塞加拉索斯数据集上，相对于基线模型LAMAP，DPL在F1上提升12%，召回率提升29%；而标准监督基线在负样本不确定时表现崩溃，仅用正例训练会错误预测所有区域。在塞浦路斯数据集（无确认负样本的纯PU设置），标准伪标签（SL）反转概率排序，而DPL恢复了有效区分能力。DPL集成模型生成可解释的概率图，支持实地调查规划，仅依赖极少标注数据即可实现有效遗址发现。
原文摘要 · Abstract (English)
Archaeological predictive modelling estimates where undiscovered sites are likely to occur by combining known locations with environmental and geospatial variables, presenting a positive-unlabeled (PU) learning challenge where confirmed sites are rare and most locations are unlabeled rather than truly negative. To overcome this, we propose asymmetric dual pseudolabeling (DPL), an end-to-end deep learning method that learns from sparse positives directly from multi-band geospatial imagery without hand-crafted feature engineering or assumptions about site absence, and evaluate on two prominent archaeological datasets. On the Sagalassos dataset, evaluated against an independent, held-out field survey, DPL outperforms the LAMAP baseline by 12% in F1 and 29% in Recall, while LAMAP maintains advantages in probability ranking. Standard supervised baselines fail catastrophically when negatives are uncertain; positive-only training collapses to predicting everywhere, es- tablishing empirical bounds. On the Cyprus dataset, a pure PU setting without confirmed negatives, SL inverts probability rankings while DPL recovers discrimination. DPL ensembles produce interpretable probability surfaces supporting survey planning, enabling effective site discovery from minimal labeled data.
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