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

Dry-stone walls hold significant heritage and environmental value. Mapping these structures is essential for ecosystem preservation and wildfire management in Australia. Yet, many walls remain unidentified due to their inaccessibility and the high cost of manual mapping. Deep learning-based segmentation offers a scalable solution, but two major challenges persist: (1) visual occlusion of low-lying walls by dense vegetation, and (2) limited labeled data for supervised training. We propose DINO-CV, a segmentation framework for automatic mapping of low-lying dry-stone walls using high-resolution Airborne LiDAR-derived digital elevation models (DEMs). DEMs overcome visual occlusion by capturing terrain structures hidden beneath vegetation, enabling analysis of structural rather than spectral cues. DINO-CV introduces a self-supervised cross-view pre-training strategy based on knowledge distillation to mitigate data scarcity. It learns invariant visual and geometric representations across multiple DEM derivatives, supporting various vision backbones including ResNet, Wide ResNet, and Vision Transformers. Applied to the UNESCO World Heritage cultural landscape of Budj Bim, Victoria, the method identifies one of Australia's densest collections of colonial dry-stone walls beyond Indigenous heritage contexts. DINO-CV 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 automated dry-stone wall mapping in vegetated and heritage-rich environments with scarce annotations.