Detalhes
Nome
Luís Carlos SecoCargo
Investigador SéniorDesde
01 novembro 2022
Nacionalidade
PortugalCentro
Computação Centrada no Humano e Ciência da InformaçãoContactos
+351222094000
luis.c.seco@inesctec.pt
2024
Autores
Canedo, D; Hipólito, J; Fonte, J; Dias, R; do Pereiro, T; Georgieva, P; Gonçalves Seco, L; Vázquez, M; Pires, N; Fábrega Alvarez, P; Menéndez Marsh, F; Neves, AJR;
Publicação
REMOTE SENSING
Abstract
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.
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