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.

This paper presents a method for the merging of lidar-derived point clouds of the same area taken at different moments, even when these are not co-registered. The workflow also incorporates the filtering of vegetation allowing the classification of unclassified point clouds using the ground points of reliable coverages. The objective is to produce a digital terrain model by joining all ground points to generate a higher resolution model than would have been possible using a single coverage. The workflow is supplemented by a multi-scale relief visualisation tool that allows for better detection of archaeological micro-reliefs of variable size even in areas of complex topography. The workflow is tested in six Iberian Iron Age sites, all of them located in mountain areas with dense Mediterranean perennial forests and shrub vegetation.

Background and Aims: In this article, visual explainers are applied to give transparency to the black box of a trained VGG19 model for the identification of multi-cell phytoliths of the Avena, Hordeum and Triticum genera. The aim is to demonstrate its proper learning by visually highlighting the phytolith characteristics that the deep learning model uses to classify these phytoliths; we then compare the model’s methods with those employed manually by archaeobotanists. Methods: The visual explainers used for this purpose are Grad-CAM, Guided Backpropagation and Guided Grad-CAM, the last being a combination of the previous two. This combined tool not only highlights the most relevant regions when classifying phytoliths on microscope images, but also emphasizes every detail within those areas. Key Results: The importance of the wave pattern as a decision-maker (key identifying characteristic) when classifying phytoliths has been demonstrated for 91 % of the microscope images. Similarly, the papillae have been a key in 86 % of Avena images, in 94 % when images included papillae, and the dendritic long-cell shape in 38 % of Triticum images. Conclusions: The analysis of the microscope images using Guided Grad-CAM has validated the established patterns in phytolith identification, such as highlighting the significance of the wave pattern. Additionally, it revealed that varying phytolith characteristics might be prominent for different genera and led to the discovery that dendritic long-cell shape, as an independent category, is also distinctive. This research is part of an effort to establish a set of computer vision best practices in computational archaeology.

We present here the first results of an ongoing research project aimed at improving our knowledge of the urban settlements of the north-eastern Iberian Peninsula during the Iron Age. In the 4th-3rd centuries BC, and probably as early as the 6th-5th centuries BC, we detect a strongly hierarchical settlement pattern in this area. It was composed of settlement types that were differentiated by their size and function. The urban sites at the top of the hierarchy are the least known, as their excavation and study present several difficulties, such as the large areas they cover (around 10 hectares) and the fact that most of them lie under dense forest cover that obscures the archaeological remains. This last factor makes it difficult to apply certain non-invasive methods, including geophysical prospection. They are, however, suitable for study by remote sensing techniques. In this paper we discuss the efficiency of those techniques, more specifically the use of lidar data as a method of detecting architectural features in these settlements.