AI 中文总结
本文提出一种可解释多模态AI框架，用于考古传感工作流的自适应校准、质量评估与采集支持，经多模态考古数据集验证可实现跨模态稳健质量评估。
AI 中文摘要
本文提出一种用于考古数字化工作流中校准监测、质量评估与自适应采集支持的多模态机器学习框架。该方法通过统一流水线跨摄影测量三维重建、高光谱成像、X射线荧光光谱与拉曼光谱运行，整合确定性质量指标、统计特征表示、机器学习分类、异常检测与可解释人工智能（XAI）。该框架不替代仪器级校准，而是引入额外算法层，评估采集是否统计一致、物理合理且适用于下游多模态集成。针对每种传感模态，采集通过编码几何、光谱、空间与统计特性的结构化特征空间表示，用于识别重建伪影、光照不一致、光谱畸变、探测器不稳定、基线波动、低信噪比等退化模式。结合监督与无监督学习方法及XAI技术，支持可接受与问题采集的自动区分及退化根本原因解释；还通过特征空间偏差关联采集级校正动作，支持自适应反馈与资源感知采集策略。在多模态考古数据集上的实验结果表明，该方法可捕捉有意义的采集变异性，实现跨异构传感模态的稳健质量评估。
英文摘要
This paper presents a multimodal machine-learning framework for calibration monitoring, quality assessment, and adaptive acquisition support in archaeological digitisation workflows. The proposed approach operates across photogrammetric 3D reconstruction, hyperspectral imaging, X-ray fluorescence spectroscopy, and Raman spectroscopy through a unified pipeline combining deterministic quality indicators, statistical feature representations, machine-learning classification, anomaly detection, and explainable artificial intelligence (XAI). Rather than replacing instrument-level calibration, the framework introduces an additional algorithmic layer that evaluates whether acquisitions are statistically consistent, physically plausible, and suitable for downstream multimodal integration. For each sensing modality, acquisitions are represented through structured feature spaces encoding geometric, spectral, spatial, and statistical properties. These representations are used to identify degradation patterns such as reconstruction artefacts, illumination inconsistencies, spectral distortions, detector instability, baseline fluctuations, and low signal-to-noise conditions. Supervised and unsupervised learning methods are combined with XAI techniques to support both automatic discrimination between acceptable and problematic acquisitions and interpretation of the underlying causes of degradation. The framework additionally supports adaptive feedback and resource-aware acquisition strategies by linking feature-space deviations to acquisition-level corrective actions. Experimental results obtained on multimodal archaeological datasets demonstrate that the proposed methodology captures meaningful acquisition variability and enables robust quality assessment across heterogeneous sensing modalities.
