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.

Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, using directly the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.

Resumo: Com este estudo apresenta-se uma nova perspectiva tridimensional do Castelo Velho de Safara (Moura), um dos grandes povoados muralhados da Idade do Ferro/período Romano Republicano existentes na linha do Rio Guadiana, criada a partir de um levantamento de alta resolução realizado com sensor LiDAR acoplado a um drone. O resultado obtido permitiu a definição detalhada da planta da muralha, o que implicou a revisão da superfície de ocupação, que agora é estimada em cerca de 1.36 hectares. Foram também identificados vários elementos inéditos, como a zona da entrada e eventuais soluções de reforço defensivo. Os dados obtidos para o Castelo Velho de Safara demonstram o potencial dos dados LiDAR para o reconhecimento das características topográficas deste tipo de recintos fortificados, cuja visibilidade das estruturas nem sempre é possível a olho nu. Palavras-chave: Idade do Ferro; Período Romano Republicano; Estruturas defensivas; Drone; LiDAR. Abstract: This study presents a new tridimensional perspective on Castelo Velho de Safara (Moura), one of the great walled settlements of the Iron Age/Roman Republic by the Guadiana River, obtained through a high-resolution survey using a drone integrated with a LiDAR sensor. The outline of the walls was defined in more detail, which meant revising the occupation area, now estimated at circa 1.36 hectares. Several unknown elements were detected, such as the entrance area and other possible defensive structures. The data obtained for the Castelo Velho de Safara demonstrate the potential of LiDAR for understanding the topographical characteristics of this type of fortified enclosure, whose structural remains are not always clear to the naked eye. Keywords: Iron Age; Roman Republic; Defensive structures; Drone; LiDAR; Odyssey.