Diamanti et al. (2024) is a significant contribution to the field of underwater robotics and their use in archaeology, with an innovative approach to some major problems in the deployment of said technologies. It identifies issues when it comes to approaching Underwater Cultural Heritage (UCH) sites and does so through an interest in the combination of data, maneuverability, and the interpretation provided by the instruments that archaeologists operate. The article's motives are clear: it is not enough to find the means to reach these sites, but rather is fundamental to take a step forward in methodology and how we can safeguard certain aspects of data recovery with robust mission planning.

To this end, the article does not fail to highlight previous contributions, in an intertwined web of references that demonstrate the marked evolution of the use of Unmanned Underwater Vehicles (UUVs), Remote Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs) and Autonomous Surface Vehicles (ASVs), which are growing exponentially in use (see Kapetanović et al. 2020). It should be emphasized that the notion of ‘aquatic environment’ used here is quite broad and is not limited to oceanic or maritime environments, which allows for a larger perspective on distinct technologies that proliferate in underwater archaeology. There is also a relevant discussion on the typologies of sensors and how these autonomous vehicles obtain their data, where are debated Inertial Measurement Units (IMU) and LiDAR systems. 

Thus, the authors of this article propose the creation of a model that acquires data through simulations, which allows for a better understanding of what a real mission presupposes in the field. Their tripartite method - pre-mission planning; mission plan and post-mission plan - offers a performing algorithm that simplifies and provides reliability to all the parts of the intervention. The use of real cases to create simulation models allows for a substantial approximation to common practice in underwater environments. And yet, the article is at its most innovative status when it combines all the elements it sets out to explore. It could simply focus on the methodological or planning component, on obtaining data, or on theoretical problems. But it goes further, which makes this approach more complete and of interest to the archaeological community. By not taking any part as isolated, the problems and possible solutions arising from the course of the mission are carried over from one parameter to another, where details are worked upon and efficiency goals are set.

One of the most significant cases is the tuning of ocean optics in aquatic environments according to the idiosyncracies of real cases (Diamanti et al. 2024: 8), a complex endeavor but absolutely necessary in order to increase the informative potential of the simulation. The exploration of various data capture models is also welcome, for the purposes of comparison and adaptation on a case-by-case basis. The brief theoretical reflection offered at the end of the article dwells in all these points and problematizes the difference between terrestrial and aquatic archaeology. In fact, the distinction does not only exist in the technical component, as although it draws in theoretical elements from archaeology that is carried out on land (see Krieger 2012 for this matter), the problems and interpretations are shaped by different factors and therefore become unique (Diamanti et al 2024: 15). The future, according to the authors, lies in increasing the autonomy of these vehicles so that the human element does not have to make decisions in a systematic way. It is in that note, and in order for that path to become closer to reality, that we strongly recommend this article for publication, in conjunction with the comments of the reviewers. We hope that its integrated approach, which brings together methods, theories and reflections, can become a broader modus operandi within the field of underwater robotics applied to archaeology.

References:

Diamanti, E., Yip, M., Stahl, A. and Ødegård, Ø. (2024). Advancing data quality of marine archaeological documentation using underwater robotics: from simulation environments to real-world scenarios, Zenodo, 8305098, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8305098

Kapetanović, N., Vasilijević, A., Nađ, Đ., Zubčić, K., and Mišković, N. (2020). Marine Robots Mapping the Present and the Past: Unraveling the Secrets of the Deep. Remote Sensing, 12(23), 3902. MDPI AG. http://dx.doi.org/10.3390/rs12233902

Krieger, W. H. (2012). Theory, Locality, and Methodology in Archaeology: Just Add Water? HOPOS: The Journal of the International Society for the History of Philosophy of Science, 2(2), 243–257. https://doi.org/10.1086/666956

The paper by Aline Deicke [1] is very readable, and it succeeds in presenting a still unnoticed topic in a well-structured way. It addresses the topic of “how to model social-spatial relations in antiquity”, as the title concisely implies, and makes important and interesting points about their interrelationship by drawing on latest theories of sociologists such as Martina Löw combined with digital tools, such as the CIDOC CRM-modeling. 

The author provides an introductory insight into the research history of funerary archaeology and addresses the problematic issue of not having investigated fully the placement of entities of the grave inventory. So far, the focus of the analysis has been on the composition of the assemblage and not on the positioning within this space-and time-limited context. However, the positioning of the various entities within the burial context also reveals information about the objects themselves, their value and function, as well as about the world view and intentions of the living and dead people involved in the burial. To obtain this form of qualitative data, the author suggests modeling knowledge networks using the CIDOC CRM. The method allows to integrate the spatial turn combined with aspects of the actor-network-theory. The theoretical backbone of the contribution is the fundamental scholarship of Martina Löw’s “Raumsoziologie” (sociology of space), especially two categories of action namely placing and spacing (SC1). The distinction between the two types of action enables an interpretative process that aims for the detection of meaningfulness behind the creation process (deposition process) and the establishment of spatial arrangement (find context). 

To illustrate with a case study, the author discusses elite burial sites from the Late Urnfield Period covering a region north of the Alps that stretches from the East of France to the entrance of the Carpathian Basin. With the integration of very basic spatial relations, such as “next to”, “above”, “under” and qualitative differentiations, for instance between iron and bronze knives, the author detects specific patterns of relations: bronze knives for food preparing (ritual activities at the burial site), iron knives associated with the body (personal accoutrement).

The complexity of the knowledge engineering requires the gathering of several CIDOC CRM extensions, such as CRMgeo, CRMarchaeo, CRMba, CRMinf and finally CRMsoc, the author rightfully suggests. In the end, the author outlines a path that can be used to create this kind of data model as the basis for a graph database, which then enables a further analysis of relationships between the entities in a next step. Since this is only a preliminary outlook, no corrections or alterations are needed. 

The article is an important step in advancing digital archaeology for qualitative research.

References

[1] Deicke, A. (2024). Spaces of funeral meaning. Modelling socio-spatial relations in burial contexts. Zenodo, 8310170, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8310170

The community of archaeologists, bioanthropologist and paleontologists relying on tools use-wear and dental microwear has grown in the recent years, mainly driven by the spread of confocal microscopes in the laboratories. If the diversity of microscopes is quite high, the main software used for 3D surface texture data analysis are mostly different versions of the same Mountains Map core. In addition to this software, since the beginning of 3D surface texture analysis in dental microwear, surface sensitive fractal analysis (SSFA) initially developed for industrial research (Brown & Savary, 1991) have been performed in our disciplines with the Sfrax/Toothfrax software for two decades (Ungar et al., 2003). This software being discontinued, these calculations have been integrated to the new versions of Mountains Map, with multi-core computing, full integration in the software and an update of the calculation itself.

New research based on these standard parameters of surface texture analysis will be, from now on, mainly calculated with this new add-on of Mountains Map, and will be directly compared with the important literature based on the previous software. The question addressed by Calandra et al. (2022), gathering several prominent researchers in this domain including the Mountains Map developer F. Blateyron, is key for the future research: can we directly compare SSFA results from both software?

Thanks to a Bayesian approach to this question, and comparing results calculated with both software on three different datasets (two on dental microwear, one on lithic raw materials), the authors show that the two software gives statistically different results for all surface texture parameters tested in the paper. Nevertheless, applying the new calculation to the datasets, they also show that the results published in original studies with these datasets would have been similar. Authors also claim that in the future, researchers will need to re-calculate the fractal parameters of previously published 3D surfaces and cannot simply integrate ancient and new data together.

We also want to emphasize the openness of the work published here. All datasets have been published online and will be probably very useful for future methodological works. Authors also published their code for statistical comparison of datasets, and proposed a fully reproducible article that allowed the reviewers to check the content of the paper, which can also make this article of high interest for student training.

This article is therefore a very important methodological work for the community, as noted by all three reviewers. It will certainly support the current transition between the two software packages and it is necessary that all surface texture specialists take these results and the recommendation of authors into account: calculate again data from ancient measurements, and share the 3D surface measurements on open access repositories to secure their access in the future.

References

Brown CA, and Savary G (1991) Describing ground surface texture using contact profilometry and fractal analysis. Wear, 141, 211–226. https://doi.org/10.1016/0043-1648(91)90269-Z

Calandra I, Bob K, Merceron G, Blateyron F, Hildebrandt A, Schulz-Kornas E, Souron A, and Winkler DE (2022) Surface texture analysis in Toothfrax and MountainsMap® SSFA module: Different software packages, different results? Zenodo, 7219877, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7219877

Ungar PS, Brown CA, Bergstrom TS, and Walker A (2003) Quantification of dental microwear by tandem scanning confocal microscopy and scale-sensitive fractal analyses. Scanning: The Journal of Scanning Microscopies, 25, 185–193. https://doi.org/10.1002/sca.4950250405

This well-written and interesting paper ‘Survey Planning, Allocation, Costing and Evaluation (SPACE) Project: Developing a Tool to Help Archaeologists Conduct More Effective Surveys’ deals with the development of a ‘modular, accessible, and simple web-based platform for survey planning and quality assurance’ in the area of pedestrian field survey methods (Banning et al. 2024).

Although there have been excellent treatments of statistics in archaeological field survey (among which various by the first author: Banning 2020, 2021), and there is continuous methodological debate on platforms such as the International Mediterranean Survey Workshop (IMSW), in papers dealing with the current development and state of the field (Knodell et al. 2023), good practices (Attema et al. 2020) or the merits of a quantifying approach to archaeological densities (cf. de Haas et al. 2023), this paper rightfully addresses the lack of rigorous statistical approaches in archaeological field survey. As argued by several scholars such as Orton (2000), this mainly appears the result of lack of knowledge/familiarity/resources to bring in the required expertise etc. with the application of seemingly intricate statistics (cf. Waagen 2022). In this context this paper presents a welcome contribution to the feasibility of a robust archaeological field survey design. 

The SPACE application, under development by the authors, is introduced in this paper. It is a software tool that aims to provide different modules to assist archaeologists to make calculations for sample size, coverage, stratification, etc. under the conditions of survey goals and available resources. In the end, the goal is to ensure archaeological field surveys will attain their objectives effectively and permit more confidence in the eventual outcomes. The module concerning Sweep Widths, an issue introduced by the main author in 2006 (Banning 2006) is finished; the sweep width assessment is a methodology to calibrate one’s survey project for artefact types, landscape, visibility and person-bound performance, eventually increasing the quality (comparability) of the collected samples. This is by now a well-known calibration technique, yet little used, so this effort to make that more accessible is certainly laudable. An excellent idea, and another aim of this project, is indeed to build up a database with calibration data, so applying sweep-width corrections will become easier accessible to practitioners who lack time to set up calibration exercises. 

It will be very interesting to have a closer look at the eventual platform and to see if, and how, it will be adapted by the larger archaeological field survey community, both from an academic research perspective as from a heritage management point of view. I happily recommend this paper and all debate relating to it, including the excellent peer reviews of the manuscript by Philip Verhagen and Tymon de Haas (available as part of this PCI recommendation procedure), to any practitioner of archaeological field survey.

References

Attema, P., Bintliff, J., Van Leusen, P.M., Bes, P., de Haas, T., Donev, D., Jongman, W., Kaptijn, E., Mayoral, V., Menchelli, S., Pasquinucci, M., Rosen, S., García Sánchez, J., Luis Gutierrez Soler, L., Stone, D., Tol, G., Vermeulen, F., and Vionis. A. 2020. “A guide to good practice in Mediterranean surface survey projects”, Journal of Greek Archaeology 5, 1–62. https://doi.org/10.32028/9781789697926-2

Banning, E.B., Alicia L. Hawkins, S.T. Stewart, Sweep widths and the detection of artifacts in archaeological survey, Journal of Archaeological Science, Volume 38, Issue 12, 2011, Pages 3447-3458. https://doi.org/10.1016/j.jas.2011.08.007 

Banning, E.B. 2020. Spatial Sampling. In: Gillings, M., Hacıgüzeller, P., Lock, G. (eds.) Archaeological Spatial Analysis. A Methodological Guide. Routledge.

Banning, E.B. 2021. Sampled to Death? The Rise and Fall of Probability Sampling in Archaeology. American Antiquity, 86(1), 43-60. https://doi.org/10.1017/aaq.2020.39

Banning, E. B. Steven Edwards, & Isaac Ullah. (2024). Survey Planning, Allocation, Costing and Evaluation (SPACE) Project: Developing a Tool to Help Archaeologists Conduct More Effective Surveys. Zenodo, 8072178, ver. 9 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8072178

Knodell, A.R., Wilkinson, T.C., Leppard, T.P. et al. 2023. Survey Archaeology in the Mediterranean World: Regional Traditions and Contributions to Long-Term History. J Archaeol Res 31, 263–329 (2023). https://doi.org/10.1007/s10814-022-09175-7 

Orton, C. 2000. Sampling in Archaeology. Cambridge University Press. https://doi.org/10.1017/CBO9781139163996

Waagen, J. 2022. Sampling past landscapes. Methodological inquiries into the bias problems of recording archaeological surface assemblages. PhD-Thesis. https://hdl.handle.net/11245.1/e9cb922c-c7e4-40a1-b648-7b8065c46880 

de Haas, T., Leppard, T. P., Waagen, J., & Wilkinson, T. (2023). Myopic Misunderstandings? A Reply to Meyer (JMA 35(2), 2022). Journal of Mediterranean Archaeology, 36(1), 127-137. https://doi.org/10.1558/jma.27148

In this paper (Linsel/Bullenkamp/Mara 2024), the authors propose an automatic system for scar-ridge-pattern detection on palaeolithic artefacts based on Morse Theory. Scare-Ridge pattern recognition is a process that is usually done manually while creating a drawing of the object itself. Automatic systems to detect scars or ridges exist, but only a small amount of them is utilizing 3D data. In addition to the scar-ridges detection, the authors also experiment in automatically detecting the operational sequence, the temporal relation between scars and ridges. As a result, they can export a traditional drawing as well as graph models displaying the relationships between the scars and ridges.

After an introduction to the project and the practice of documenting palaeolithic artefacts, the authors explain their procedure in automatising the analysis of scars and ridges as well as their temporal relation to each other on these artefacts. To illustrate the process, an open dataset of lithic artefacts from the Grotta di Fumane, Italy, was used and 62 artefacts selected. To establish a Ground Truth, the artefacts were first annotated manually. The authors then continue to explain in detail each step of the automated process that follows and the results obtained.

In the second part of the paper, the results are presented. First the results of the segmentation process shows that the average percentage of correctly labelled vertices is over 91%, which is a remarkable result. The graph modelling however shows some more difficulties, which the authors are aware of. To enhance the process, the authors rightfully aim to include datasets of experimental archaeology in the future. They also aim to develop a way of detecting the operational sequence automatically and precisely.

This paper has great potential as it showcases exactly what Digital and Computational Archaeology is about: The development of new digital methods to enhance the analysis of archaeological data. While this procedure is still in development, the authors were able to present a valuable contribution to the automatization of analytical archaeology. By creating a step towards the machine-readability of this data, they also open up the way to further steps in machine learning within Archaeology.

Bibliography

Linsel, F., Bullenkamp, J. P., and Mara, H. (2024). Linking Scars: Topology-based Scar Detection and Graph Modeling of Paleolithic Artifacts in 3D, Zenodo, 8296269, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8296269