MC-H-Geo: A Multi-Scale Contextual Hierarchical Framework for Fine-Grained Lithology Classification

High-resolution lithological mapping of outcrops is fundamental for reservoir characterization and petroleum geology, yet distinguishing lithologies with subtle petrophysical contrasts remains a major challenge. This study proposes MC-H-Geo, a multi-scale contextual hierarchical framework for automated lithology classification from terrestrial laser scanning (TLS) point clouds. The framework integrates three modules: (i) a multi-scale contextual feature engine that extracts spectral, geometric, and textural descriptors across local and stratigraphic contexts, enhanced by cross-scale differentials to capture stratigraphic variability; (ii) a gated expert classifier with task-adaptive feature subsets for hierarchical vegetation-rock and intra-rock discrimination; and (iii) a two-step geological post-processing procedure that enforces stratigraphic continuity through Z-axis correction and neighborhood smoothing. Experiments on the Qianwangjiahe outcrop (Ordos Basin, China) demonstrate state-of-the-art performance (OA = 94.3%, Macro F1 = 0.944), outperforming PointNet++ (77.1%), SG-RFGeo (74.2%), and XGBoost (61.7%). Error analysis reveals that residual sandstone-vegetation confusion results from feature aliasing in weathered zones, highlighting the intrinsic limitations of TLS-only data. Overall, MC-H-Geo establishes an advanced framework for fine-grained lithological mapping and identifies multi-sensor data fusion as a promising pathway toward robust, geologically consistent outcrop interpretation.