From LiDAR to deep learning: A case study of computer-assisted approaches to the archaeology of Guadalupe and northeast Honduras

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mike Lyons, Franziska Fecher, M. Reindel
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引用次数: 0

Abstract

Abstract 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.
从激光雷达到深度学习:瓜达卢佩和洪都拉斯东北部考古计算机辅助方法的案例研究
考古学家对在物质文化的基础上更好地理解人类的过去很感兴趣。我们用来解决考古研究问题的工具从泥铲和刷子到最近的人工智能。随着时间的推移,计算机技术的普及,计算机辅助考古学方法的广度也在增加。这种扩散为我们提供了一个相当大的工具集,用于解决新的和长期存在的问题,特别是考古学家,计算机科学家和工程师之间的跨学科合作不断增长。瓜达卢佩/Colón考古项目是采用计算机方法的考古项目的一个例子。项目应用和方法范围从使用地理信息系统(GIS)或光探测和测距(LiDAR)的区域尺度站点识别到使用卷积神经网络对陶瓷材料进行分类的微观尺度。还探讨了与地点、特征和人工制品的3D建模相关的方法及其益处。本文概述了该项目使用的方法,包括:1)使用GIS坡度分析进行预测建模,以确定可能的遗址位置;2)用于遗址测绘和表征的运动结构(SfM)无人机图像;3)用于遗址识别、测绘和表征的机载激光雷达;4)用于改进可视化的石头特征3D建模;5)用于更有效记录的陶瓷文物3D建模;6)深度学习在陶瓷材料薄壁自动分类中的应用。这些方法被讨论和批判性地考虑,并理解工程学、计算机科学和考古学领域专家之间的跨学科合作是改进和扩展考古学整体数字方法的重要手段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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