Digital imaging techniques for recording and analysing prehistoric rock art panels in Galicia (NW Iberia)

Several works have highlighted the relevance of 3D modelling techniques for the study of rock art, especially in case of deteriorated state of preservation. This paper presents a methodological approach to accurate document two Bronze Age rock art panels in Galicia (Spain), using photogrammetry SfM. The main aim is to show the application of digital enhancement techniques which have allowed the accurate depiction of the motifs and the correction of previous calques, focusing on the application of the exaggerated shading as a novel analytical method

Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia

This paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available

Hybrid MSRM-based deep learning and multitemporal Sentinel 2-based machine learning algorithm detects near 10k archaeological tumuli in north-western Iberia

This is the final version. Available on open access from MDPI via the DOI in this record.Data Availability Statement: All relevant material has been made available as Supplementary MaterialsThis paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available.European Union Horizon 2020Spanish Ministry of Science, Innovation and UniversitiesFundación BBV

Domesticating light and shadows in the Neolithic: the Dombate passage grave (A Coruña, Spain)

International audienc