Mapping Hidden Heritage: Self-supervised Pre-training for Archaeological Stone Wall Mapping in Historic Landscapes Using High-Resolution DEM Derivatives

Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management during dry seasons in Australia. However, many of these stone structures in remote or vegetated landscapes remain undocumented due to limited accessibility and the high cost of manual mapping. Deep learning-based segmentation offers a scalable approach for automated mapping of such features, but challenges remain: the visual occlusion of low-lying walls by dense vegetation and the scarcity of labeled training data. This study presents DINO-CV, a self-supervised cross-view pre-training framework based on knowledge distillation, designed for accurate mapping of dry-stone walls using high-resolution Digital Elevation Models (DEMs) derived from airborne LiDAR. By learning invariant structural representations across multiple DEM-derived views, specifically Multi-directional Hillshade (MHS) and Visualization for Archaeological Topography (VAT), DINO-CV addresses both occlusion and data scarcity challenges. Applied to the Budj Bim Cultural Landscape (Victoria, Australia), a UNESCO World Heritage site, the approach achieves a mean Intersection over Union (mIoU) of 68.6% on test areas and maintains 63.8% mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for large-scale, automated mapping of cultural heritage features in complex and vegetated environments. Beyond archaeology, this approach offers a scalable solution for environmental monitoring and heritage preservation across inaccessible or environmentally sensitive regions.