Uncovering Archaeological Sites In Airborne LiDAR Data With Data-Centric Artificial Intelligence

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Canedo, Daniel, Fonte, Joao, Seco, Luis Goncalves, Vazquez, Marta, Dias, Rita, Do Pereiro, Tiago, Hipolito, Joao, Menendez-Marsh, Fernando, Georgieva, Petia, Neves, Antonio J. R.
• Format: Article
• Language: English
• Subjects: Annotations; Archaeology; Artificial intelligence; Data acquisition; Data augmentation; Data-centric Artificial Intelligence; Datasets; Deep Learning; Digital imaging; Historic buildings & sites; Historic sites; Image segmentation; Land use; Laser radar; LiDAR; Location-Based Ranking; Mapping; Morphology; Object Detection; Object linking & embedding; Project planning; Proposals; Remote sensing; Vegetation mapping
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3290305

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.