Mapping Hidden Heritage: Self-supervised Pre-training on High-Resolution LiDAR DEM Derivatives for Archaeological Stone Wall Detection

TL;DR

This paper introduces DINO-CV, a self-supervised pre-training framework that leverages high-resolution LiDAR-derived DEMs to accurately and efficiently detect archaeological stone walls in vegetated and inaccessible landscapes, reducing the need for extensive labeled data.

Contribution

The study presents a novel self-supervised learning method, DINO-CV, that effectively maps dry-stone walls using DEM derivatives, addressing occlusion and data scarcity challenges in archaeological mapping.

Findings

Achieved 68.6% mIoU on test areas.

Maintained 63.8% mIoU with only 10% labeled data.

Demonstrated scalability for large-scale heritage mapping.

Abstract

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: 1.the visual occlusion of low-lying dry-stone walls by dense vegetation and 2.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 and data-efficient mapping of dry-stone walls using Digital Elevation Models (DEMs) derived from high-resolution airborne LiDAR. By learning invariant geometric and geomorphic features across DEM-derived views, (i.e., Multi-directional Hillshade and Visualization for Archaeological Topography), DINO-CV addresses the occlusion by vegetation and data scarcity challenges. Applied to the Budj Bim Cultural Landscape at 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.