Appearance Meets Geometry: Deep Learning for Semantic Segmentation of Archaeological Fortification Masonry

Description

Abstract

Modern archaeological practice faces a critical bottleneck: while photogrammetric surveys routinely generate terabytes of high-resolution 3D data from ancient fortifications, manual interpretation methods remain time-consuming and subjective, leaving much documentation underexploited. This paper presents a novel machine-learning workflow that bridges the gap between high-throughput data acquisition and efficient analysis by leveraging both appearance and geometric information from 3D photogrammetric models for automated masonry semantic segmentation.
Our approach transforms 3D documentation into a dual-layer format combining high-resolution orthomosaics with geometry-based normal maps derived from heightmap data. We trained convolutional neural networks on annotated datasets from Carian fortification walls currently spanning approximately 700 m across the ancient sites of Halicarnassus and Cedreae. The training dataset comprises 1,876 m² of wall facade with 5,560 individually annotated stones across several masonry classes.
Comparative evaluation of appearance-only, geometry-only, and combined models shows consistent performance advantages for the integrated approach. In an ensemble evaluation over five independent training runs per variant, the combined model improved mean stone IoU by 10% over the geometry-only and 4% over the appearance-only model, with this ordering consistent across all individual runs. These results confirm that visual and geometric features provide complementary information essential for robust archaeological analysis. The automated approach enables processing of kilometer-long fortifications in minutes of computation, compared to the weeks of manual drawing and classification that traditional methods require. This transforms archaeological documentation from selective sampling to comprehensive analysis of entire defensive systems. This work represents a concrete step toward realizing photogrammetry's analytical potential, enabling systematic comparative studies across sites and periods at unprecedented scales.

Keywords: semantic segmentation, photogrammetry, normal maps, fortification architecture, deep learning, machine learning, masonry classification, 3D documentation, Carian fortifications