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.

Sixty-six new archaeological sites have been discovered thanks to the combined use of different remote sensing techniques and open access geospatial datasets (mainly aerial photography, satellite imagery, and airborne LiDAR). These sites enhance the footprint of the Roman military presence in the northern fringe of the River Duero basin (León, Palencia, Burgos and Cantabria provinces, Spain). This paper provides a detailed morphological description of 66 Roman military camps in northwestern Iberia that date to the late Republic or early Imperial eras. We discuss the different spatial datasets and GIS tools used for different geographic contexts of varied terrain and vegetation. Finally, it stresses out the relevance of these novel data to delve into the rationale behind the Roman army movements between the northern Duero valley and the southern foothills of the Cantabrian Mountains. We conclude that methodological approaches stimulated by open-access geospatial datasets and enriched by geoscientific techniques are fundamental to understand the expansion of the Roman state in northwestern Iberia during the 1st c. BC properly. This renewed context set up a challenging scenario to overcome traditional archaeological perspectives still influenced by the cultural-historical paradigm and the pre-eminence of classical written sources.

GIS-based digital modelling tools, such as the well-known least cost paths (LCP), have been widely used in archaeology in recent years as ways of approaching forms of mobility in the past. Roman roads are among the best-known examples of ancient networks of paths and have been widely studied using such approaches. In this paper, we shall make a general reflection on the applicability of those tools for the modelling and analysis of ancient routes, with a special focus on Roman roads. Drawing from a case study in the NW Iberian Peninsula, we shall discuss certain aspects related to the potential and limits of Cumulative Costs, LCP and other related tools for the modelling and analysis of ancient roads. We will illustrate how the use of tools which explore potential mobility in less restricted ways can help to overcome some of the limitations of LCP.

GIS are an essential element in archaeology. Their use has become widespread for their potential to store, reference, analyse and visualise spatial information. Nonetheless, to the best of our knowledge, a systematic review of academic peer-reviewed publications related to the use of GIS, as a framework, in archaeology has never been presented before. Our goal in this work is to identify what has been published so far in relation to using GIS in archaeology within a small selected sample. We used the PRISMA guideline to perform a systematic review of 671 publications that we identified using the SCOPUS database and the keywords ‘GIS’ and ‘archaeology’. The collected publications were screened, analysed, and categorized into different relevant categories. Our analysis shows that GIS, in our selected sample, are mostly used for visualization and information management tasks. Moreover, spatial analysis studies were more common than other studies, and theoretical publications are scarce. The lack of a theoretical background in GIS may be the cause of some of the problems related to GIS applications in archaeology.

The increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal’s Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm’s ability to distinguish genuine sites. The improved algorithm was then tested in two areas: the original Alto Minho validation region and the Barbanza region in Spain, where the location of burial mounds was well established through prior field work.