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it - Information Technology 64(6) - Dezember 2022

https://dl.gi.de/handle/20.500.12116/40496
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Auflistung it - Information Technology 64(6) - Dezember 2022 nach Autor:in "Fecher, Franziska"

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  • Zeitschriftenartikel
    From LiDAR to deep learning: A case study of computer-assisted approaches to the archaeology of Guadalupe and northeast Honduras
    (it - Information Technology: Vol. 64, No. 6, 2022) Lyons, Mike; Fecher, Franziska; Reindel, Markus
    Archaeologists are interested in better understanding matters of our human past based on material culture. The tools we use to approach archaeological research questions range from the trowel and brush to, more recently, even those of artificial intelligence. As access to computing technology has increased over time, the breadth of computer-assisted methods in archaeology has also increased. This proliferation has provided us a considerable toolset towards engaging both new and long-standing questions, especially as interdisciplinary collaboration between archaeologists, computer scientists, and engineers continues to grow. As an example of an archaeological project engaging in computer-based approaches, the Guadalupe/Colón Archaeological Project is presented as a case study. Project applications and methodologies range from the regional-scale identification of sites using a geographic information system (GIS) or light detection and ranging (LiDAR) down to the microscopic scale of classifying ceramic materials with convolutional neural networks. Methods relating to the 3D modeling of sites, features, and artifacts and the benefits therein are also explored. In this paper, an overview of the methods used by the project is covered, which includes 1) predictive modeling using a GIS slope analysis for the identification of possible site locations, 2) structure from motion (SfM) drone imagery for site mapping and characterization, 3) airborne LiDAR for site identification, mapping, and characterization, 4) 3D modeling of stone features for improved visualization, 5) 3D modeling of ceramic artifacts for more efficient documentation, and 6) the application of deep learning for automated classification of ceramic materials in thin section. These approaches are discussed and critically considered with the understanding that interdisciplinary cooperation between domain experts in engineering, computer science, and archaeology is an important means of improving and expanding upon digital methodologies in archaeology as a whole.