Theory and practice for an object-based approach in archaeological remote sensing

https://doi.org/10.1016/j.jas.2019.04.005Get rights and content

Highlights

  • New methodological workflow for standardizing object-based applications in archaeology.

  • Theoretical framework linking ontologies and (semi)automated image-classification.

  • Introduction of the temporal variable in the formalization of expert knowledge.

  • Multi-scale case studies of Archaeological Object-based Image Analysis (ArchaeOBIA).

Abstract

Object-based image analysis (OBIA) is rapidly emerging as a valuable method for integrating the data processing techniques and GIS approaches classically employed in archaeology. OBIA is intended to replicate human perception by using a protocol of (semi)automated image segmentation and classification. However, the lack of a theoretical background adapted to the specificities of the archaeological discipline is still preventing researchers from finding a shared language and a common protocol of investigation necessary to allow the comparability of the results.

This article discusses a series of crucial theoretical issues linked to the incompleteness and the equi-/multi-finality of the archaeological record and introduces the core concept of Diachronic Semantic Models (DhSM) as a means to integrate the long-term evolution of the archaeological landscape in the conceptual, digital and real-world frameworks of the object-based approach.

We also present an assessment of the limits and potential of this method, built from a set of case studies from published and unpublished research. Finally, we propose a general workflow of an Archaeological Object-Based Image Analysis (ArchaeOBIA) project, designed for stimulating the development of an operational routine for object-based applications in archaeology.

Introduction

In recent years, archeological research has seen an increasing number of remote sensing (RS) applications with the use of new sensors and data types, such as multi/hyper-spectral imagery (Traviglia, 2011; Lasaponara, Masini, 2012; Doneus et al., 2014; Agapiou et al., 2014; De Guio, 2015; Moriarty et al., 2019), radar (Wiseman, El-Baz, 2007; Lasaponara, Masini, 2013; Chen et al., 2016; Tapete, Cigna, 2017; Burigana, Magnini, 2018) and LiDAR data (Bewley et al., 2005; Devereux et al., 2005; Doneus et al., 2008; Challis et al., 2011; Opitz, Cowley, 2013), that have joined the classic aerial photographs. However, these innovations had only little impact on the traditional photointerpretation, which remains essentially a work for the human operator via visual inspection (Brophy, Cowley, 2005; Cowley, 2015; Crutchley, 2015; Wilgocka et al., 2016; Quintus et al., 2017).

The reduction of the instrumental costs and the exponential increase in the volume of datasets of the last few years prompts for an overall revision of the methods traditionally used in archaeology (Bennett et al., 2014). In this context, the automation or semi-automation of image analysis seems to offer an opportunity to speed up and grant better reproducibility for the classification and the subsequent interpretation of the remotely sensed imagery, even in the archaeological field. This approach has a long history in the domains of environmental, material and biomedical sciences (Heidrich et al., 2013; Caie et al., 2016; Feuchtinger et al., 2016; Hawkins et al., 2016), but its potential is yet to be fully exploited for systematic research on cultural heritage.

As previously pointed out, the number of papers dealing with object, pattern and scenery recognition (OPSR) of archaeological contexts is still very limited and includes applications of template matching, machine learning, convolutional neural network (CNN), custom algorithms and object-based methods (see Traviglia, Torsello, 2017 and Davis, 2018 for a general overview on the topic). In this paper, we will focus our discussion on object-based image analysis (OBIA or GeOBIA, with a geographic connotation) which was described as “an evolving paradigm with specific tools, software, methods, rules, and language (that) is increasingly being used in studies which need to conceptualize and formalize knowledge representing location based reality” (Blashke et al., 2014).

In order to promote the interoperability of the rule-sets, it is necessary to make explicit what it is implicit in the classification and interpretation process. For this purpose, we propose a theoretical framework aimed at formalizing expert archaeological knowledge using ontologies (i.e. formal, explicit specifications of a shared conceptualization, according to Gruber, 1993). Moreover, we introduce the concept of Diachronic Semantic Models (DhSM), developed to better explain the long-term evolution of the landscape in machine-readable language (§ 4.1).

As object-based applications make their way into archaeological practice, it becomes increasingly important to find a shared language and a common protocol of investigation, ideally passing from operational practice to operational routine. In this paper, we suggest a general workflow for OBIA applications in archaeology built from a wide range of published and unpublished case-studies to ease the comparability of data. Finally, we argue that there is a growing urgency to find a common way for publishing rule-sets and rule-set libraries to be semi-automatically or automatically implemented for archaeological investigations. This topic is of general interest for the OBIA community, but it should be stressed with a particular emphasis in view of further increasing the role of object-based applications in archaeology, as most of the operators have a humanistic background and, consequently, longer learning times in the development of customized rule-sets.

Section snippets

The OBIA approach

RS imagery are composed of pixels (or voxels, in a 3D coordinate system), whose dimension is a function of the sensor used and of the parameters employed for the acquisition (Gonzalez et al., 2008). While pixel-based classifications rely only on the information contained in each single pixel (Lillesand et al., 2004), the basic entity of OBIA is represented by image-objects (sometimes also called image-segments) (Hay et al., 2001; Blaschke et al., 2004). An image-object is “a discrete region of

Practical issues

Despite the strengths and opportunities offered by OBIA, a series of drawbacks and weakness must be taken into account when considering the slow emergence of this method in archaeological RS. The criticism of conservative archaeologists has significantly slowed down in the last few years as the technological advances in the field of computer-aided OPSR have demonstrated their significant contribution to the research (Bennett et al., 2014); however, a series of practical issues is still

Conclusions and perspectives

OBIA is a growing trend in archaeological RS. In this paper, we offered a synthesis of its basic principles and discussed the most relevant papers dealing with archaeological case studies. Moreover, we showed the practical problems which still preclude a wider diffusion of the method among the archaeological RS community and the field operators. It was argued that some of these issues can be overcome by introducing a theoretical framework able to formalize expert knowledge. The incompleteness

Author contributions

Conceptualization, L.M. and C.B.; methodology, L.M. and C.B.; software, L.M.; validation, C.B. and L.M.; investigation, L.M.; data curation, L.M.; writing—original draft preparation, C.B.; writing—review and editing, L.M.; visualization, C.B.; project administration, L.M.

Funding

This research was carried out within the framework of the postdoctoral grant “Remote diagnostic for monitoring endangered archaeological landscapes” funded by the project Horus 2.0 (Department of Cultural Heritage, University of Padova).

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgments

Numerous colleagues have contributed to the debate on the issues presented in this paper, but major thanks are due to Armando De Guio for the constant inspiration and scientific support. The authors would also like to thank the anonymous referees whose critical reading helped in improving the accepted draft of the manuscript.

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