Rock art offers traces of our most remote past and was made with mineral and organic substances in shelters, walls, or the ceilings of caves. As it is notably fragile, it is fortunate that some instances remain intact—but a variety of natural and anthropogenic factors can lead to its disappearance. Therefore, as a valuable cultural heritage, rock art requires special conservation and protection measures. Geomatic remote-sensing technologies such as 3D terrestrial laser scanning (3DTLS), drone flight, and ground-penetrating radar (GPR) allow us to generate exhaustive documentation of caves and their environment in 2D, 2.5D, and 3D. However, only its combined use with 3D geographic information systems (GIS) lets us generate new cave maps with details such as overlying layer thickness, sinkholes, fractures, joints, and detachments that also more precisely reveal interior–exterior interconnections and gaseous exchange; i.e., the state of senescence of the karst that houses the cave. Information of this kind is of great value for the research, management, conservation, monitoring, and dissemination of cave art.

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 geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. 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 on manually segmented images to explore automated crack detection 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 traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts.

Previous efforts to link Palaeolithic cultural records to specific populations through DNA analysis have focused on materials from archaeological floor deposits such as bones, sediments, and artefacts. In this study, we explore whether rock art, a spatially distinct expression of human activity, can also preserve DNA traces from its creators. We analyse DNA preservation in pigment samples collected in and around 24 rock art panels from 11 caves across Spain and Portugal, including simple marks (from nine sites), hand stencils (Maltravieso Cave, Extremadura, Spain), and figurative paintings (Cave of Altamira, Cantabria, Spain). We recover traces of ancient human mitochondrial and nuclear DNA, unaccompanied by faunal DNA, from a pigmented calcite crust at Escoural Cave (Portugal), as well as from an unpigmented cave wall sample from the same site. The absence of faunal DNA in both samples suggests direct DNA deposition through human contact. In contrast, three additional unpigmented samples, from Escoural and Covarón Cave (Asturias, Spain), yielded mixtures of human and faunal DNA, suggesting indirect deposition. Although our results do not conclusively link ancient human DNA preservation to the generation of cave art, we show that traces of human DNA can persist on cave walls for thousands of years.

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.