Knowledge-Enhanced Multimodal Reasoning Framework for Archaeological Site Identification on the Northwestern Margin of the Tarim Basin

Abstract

To address visual misclassification caused by subtle surface signatures, variable preservation, and similar patterns from natural landforms and modern features, we propose a knowledge-enhanced multimodal reasoning framework for the northwestern margin of the Tarim Basin. Site Objects organize multi-source data, while expert knowledge from the Morphology, Shadow, Context, and Temporal dimensions is represented as structured evidence and hierarchical decision rules. Knowledge-guided supervision and domain adaptation using low-rank adaptation enable candidate assessments to consider archaeological support, non-archaeological alternative explanations, and insufficient evidence. Independent testing showed that knowledge-informed prompts alone did not consistently improve performance. After domain adaptation, strict precision increased from 22.31% to 58.70% and the F1 score from 33.54% to 62.79%, while strict recall remained essentially unchanged. Strict false-positive rates for modern anthropogenic features and natural geomorphic look-alikes decreased from 53.75% and 31.25% to 10.00% and 7.50%, respectively, while candidate recall improved. Regional application demonstrated candidate screening across contiguous remote sensing scenes. The framework retains potential sites while constraining visually similar confounders, thus providing more reliable and traceable screening support for large-scale surveys in complex arid regions.

The authors' abstract, as published at the source. Remote Sensing, 2026 · DOI ↗