Filling the Void in Archaeological Excavations: 2D Point Clouds to 3D Volumes

Open Archaeology 2021; 7: 589–614 Research Article Gary R. Nobles*, Christopher H. Roosevelt Filling the Void in Archaeological Excavations: 2D Point Clouds to 3D Volumes https://doi.org/10.1515/opar-2020-0149 received December 7, 2020; accepted May 31, 2021 Abstract: 3D data captured from archaeological excavations are frequently left to speak for themselves. 3D models of objects are uploaded to online viewing platforms, the tops or bottoms of surfaces are visualised in 2.5D, or both are reduced to 2D representations. Representations of excavation units, in particular, often remain incompletely processed as raw surface outputs, unable to be considered individual entities that represent the individual, volumetric units of excavation. Visualisations of such surfaces, whether as point clouds or meshes, are commonly viewed as an end result in and of themselves, when they could be considered the beginning of a fully volumetric way of recording and understanding the 3D archaeological record. In describing the creation of an archaeologically focused recording routine and a 3D-focused data processing workflow, this article provides the means to fill the void between excavation-unit surfaces, thereby producing an individual volumetric entity that corresponds to each excavation unit. Drawing on datasets from the Kaymakçı Archaeological Project (KAP) in western Turkey, the article shows the potential for programmatic creation of volumetric contextual units from 2D point cloud datasets, opening a world of possibilities and challenges for the development of a truly 3D archaeological practice. Keywords: Kaymakçı, 3D GIS, 3D visualisation, volumetric analysis, photogrammetry 1 Introduction Recent innovations in 3D recording and the display of archaeological entities have focused primarily on the capture and visualisation of landscapes, excavations, and objects as point clouds and derived textured meshes. Innovations external to archaeology in optical laser scanning, structured light scanning, and Structure from Motion (SfM, colloquially referred to as Photogrammetry) and broader image-based data capture technologies, coupled with their increasing cost-effectiveness, have seen the spread of such technologies across the archaeological discipline (Bagi, 2018; Bevan et al., 2014; Campana, 2017; De Reu et al., 2013; De Reu et al., 2014; Doneus et al., 2011; Douglass, Lin, & Chodoronek, 2015; Kjellman, 2012; McPherron, Gernat, & Hublin, 2009; Richards-Rissetto et al., 2012; Roosevelt, Cobb, Moss, Olson, & Ünlüsoy, 2015; Willis, Koenig, & Black, 2016; Zaitceva, Vavulin, Pushkarev, & Vodyasov, 2016; Zollhöfer Article note: This article is a part of the Special Issue on Art, Creativity and Automation. Sharing 3D Visualization Practices in Archaeology, edited by Loes Opgenhaffen, Martina Revello Lami, Hayley Mickleburgh. * Corresponding author: Gary R. Nobles, Department of Geomatics, Oxford Archaeology, Janus House, Osney Mead, Oxford 0X2 OES, Oxfordshire, United Kingdom; Research Center for Anatolian Civilizations, Koç University, Rumelifeneri Yolu, Sarıyer, Istanbul, 34450, Turkey, e-mail: [email protected] Christopher H. Roosevelt: Research Center for Anatolian Civilizations, Archaeology and History of Art Department, Koç University, Rumelifeneri Yolu, Sarıyer, Istanbul, 34450, Turkey, e-mail: [email protected] ORCID: Gary R. Nobles 0000-0002-9451-5413; Christopher H. Roosevelt 0000-0002-4302-4788 Open Access. © 2021 Gary R. Nobles and Christopher H. Roosevelt, published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License. 590 Gary R. Nobles and Christopher H. Roosevelt et al., 2015; amongst many others). Despite external innovations in spatial data capture, however, techno- logical innovations internal to archaeological fieldwork methods, post-fieldwork data processing, and analytical workflows have been limited (cf. Avern & Franssens, 2012; Gavryushkina, 2018; ISAAK, 2020; Merlo & Shell, 2005). Obstacles to innovation derive in part from qualitative and quantitative differences in datasets available for experimentation and from limitations in available software. Challenges also include the differing goals of capturing 3D data from archaeological excavations. 3D data frequently must speak for themselves, with object models uploaded to online viewing platforms, surfaces visualised in 2.5D, or both reduced to 2D representations. Models of excavation units, in particular, often remain as surfaces that are unable to represent volumetric units of excavation or stratigraphy. Surface visualisations as point clouds or meshes are frequently viewed as the end of the process. For those working towards an end goal of a truly 3D Geographic Information System (GIS), however–one anchored in 3D space and intended to enable 3D spatial analysis (Merlo, 2016; van Leusen & Nobles, 2018)–the results of such visualisations could be considered just the beginning of a fully volumetric way of recording, visualising, analysing, and thinking about the 3D archaeological record. In describing the creation of an archaeologically focused recording routine and a 3D-focused data processing workflow, this article provides the means to fill the void between the surfaces of an excavation unit, thereby producing an individual volumetric entity that represents the excavation unit in 3D. Drawing on the large and robust datasets of the Kaymakçı Archaeological Project (KAP) in western Turkey, the article demonstrates the potential of a programmatic approach to the creation of volumetric contextual units from 2D point cloud datasets. Resultant datasets are 3D GIS compatible, even if truly 3D GIS software remains currently unavailable. Before describing the point-cloud-to-volume workflow itself, we review distinctions in multi-dimensional data, the importance of thinking spatially, and the methods used to produce the KAP dataset, returning in the end to a discussion of some foreseeable challenges and opportunities for the development of a truly 3D archaeological practice. 2 3D Technologies, 3D Data, and Spatial Thinking Workflows that leverage new 3D data capture methods do so adhering largely to existing and traditional ways of archaeological working and thinking. That is, the integration of developing geospatial technologies has done little to challenge traditional approaches to archaeology. Very few archaeologists use these technologies to disrupt the craft or innovate significantly to create new ways of working, thinking, inter- acting, and interpreting archaeological information. Instead, new technologies are often adopted following the development of a new tool with push button functionality and set defaults, often yielding aesthetically pleasing results that can be interpreted visually with little to no knowledge of underlying processes. This trend has a history longer than its association with 3D technologies (Gillings, 2012; Huggett, 2004; Kvamme, 1999; Lock, 2009) and is considered to serve as a divide between archaeologists who supply a digital service and digital archaeologists (Huggett, 2015a, p. 80). Thorough engagement with 3D technologies, however, requires at least the conceptual embrace of spatial thinking (see also Goodchild & Janelle, 2010; Lock & Pouncett, 2017; National Research Council, 2006, p. 12; Sinton, 2011). This requires re-evaluating the concept of space and its representations in archaeology and, in particular, how 3D geometries move beyond traditional 2D abstractions to provide a framework for new digital surrogates or analogues that represent the entirety of the archaeological record. It requires also the adoption of new tools of management, representation, and analysis that build on existing spatial database and GIS-like functionality and enable the answering of spatially derived research questions that depend on the visual manipulation and analysis of 3D entities (Lock & Pouncett, 2017; van Leusen & Nobles, 2018). Nevertheless, the field is not quite “there” yet. Current approaches to recording 3D space and subsequent workflows often, if not nearly always, produce only 2.5D geometries that are frequently reduced to 2D outputs (Ledoux & Meijers, 2011; Penninga, 2008, p. 15). The typical deliverables of a 3D workflow include orthophotos that enable 2D digitisation and conformance to conventional archaeological reporting. This represents a regression from 3D data capture to Filling the Void in Archaeological Excavations 591 2.5D data and ultimately preferences 2D spatial thinking. 3D spatial thinking can only develop if the underlying data remain truly 3D. This call to develop a theoretical framework for 3D archaeology is echoed more broadly by those calling for Digital Archaeology to be viewed as more than a technical service and actively develop its own body of theory (Gillings, 2012; Huggett, 2012, 2015a, 2015b; Perry & Taylor, 2018; Verhagen, 2018; van Leusen & Nobles, 2018; Zubrow, 2006, p. 9). Getting the most out of these data capture methods requires us to move beyond 2.5D geometric spatial representation to create true 3D geometric abstractions of space. The latest wave of remote sensing technologies captures real-world surfaces as highly detailed point clouds. Workflows developed for landscape and excavation applications usually lead to the production of 2.5D meshes and DEMs, as discussed by Verhoeven (2017). Some approaches extrude overlying meshes into 3D geometries, presenting the illusion of a fully recorded 3D space. The point clouds that result from archaeological data capture routines

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