Cave of Altamira (Spain): UAV-Based SLAM Mapping, Digital Twin and Segmentation-Driven Crack Detection for Preventive Conservation in Paleolithic Rock-Art Environments

The cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where conventional geomatics workflows are limited by severe spatial, lighting, and safety constraints. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained using manually segmented images to automatically detect cracks under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks, and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling long-term storage, multitemporal comparison, and collaborative annotation. The study confirms the feasibility of UAV-based SLAM mapping combined with videogrammetry and deep learning segmentation as a robust approach for structural assessment and preventive conservation in Paleolithic caves and similarly constrained cultural heritage contexts.