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 [1], the authors describe how they apply machine learning with YOLOv5 to classify visual data, aiming to enhance understanding of archaeological phenomena before conducting destructive fieldwork. Despite challenges, the integration of machine learning with remote sensing technology was seen as transformative, enabling precise recording of areas of interest and assessment of environmental risk factors. The paper discusses successes, failures, and future directions in machine learning research, emphasising the need for standardisation and integration of streamlined methods. The application of machine learning techniques facilitates non-destructive analysis of material culture records, improving conservation efforts and offering insights into both past and contemporary phenomena. While the initial use of YOLOv5 showed potential for consistent detection of archaeological features, further refinement and dataset enlargement are deemed necessary for broader application in non-destructive archaeological surveying. The authors advocate for the integration of machine learning tools in archaeological research to save time, resources, and promote ethical digital recording practices. They highlight the importance of standardised methodologies to enhance credibility and reproducibility, aiming to contribute to the ongoing dialogue in computational archaeology.

Overall, I think this paper is a good step forward in detecting small objects in UAV data, and contains useful information for similar studies. The aim towards greater reproducibility and standardisation is of course shared more widely in the machine learning community, and this study is a good example of how to approach this.

References

[1] Sharp, K., Christofis, B., Eslamiat, H., Nepal, U. and Osores Mendives, C. (2024). Machine Learning for UAV and Ground-Captured Imagery: Toward Standard Practices. Zenodo, 8307612, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8307612

Paleolithic archaeologists habitually measure 3-coordinate data for artifacts in their excavations. This was first done manually, and in the last three decades it is usually performed by a total station and associated hardware. While the field recording procedure is quite straightforward, visualizing and analyzing the data are not, often requiring specialized proprietary software or coding expertise. Here, Royer and colleagues (2023) present the SEAHORS application, an elegant solution for the post-excavation analysis of artifact coordinate data that seems to be instantly useful for numerous archaeologists. SEAHORS allows one to import and organize field data (Cartesian coordinates and point description), which often comes in a variety of formats, and to create various density and distribution plots. It is specifically adapted to the needs of archaeologists, is free and accessible, and much simpler to use than many commercial programs. The authors further demonstrate the use of the application in the post-excavation analysis of the Cassenade Paleolithic site (see also Discamps et al., 2019). 

While in no way detracting from my appreciation of Royer et al.’s (2023) work, I would like to play the devil’s advocate by asking whether, in the majority of cases, field recording of artifacts in three coordinates is warranted. Royer et al. (2023) regard piece plotting as “…indispensable to propose reliable spatial planimetrical and stratigraphical interpretations” but this assertion does not hold in all (or most) cases, where careful stratigraphic excavation employing thin volumetric units would do just as well.

Moreover, piece-plotting has some serious drawbacks. The recording often slows excavations considerably, beyond what is needed for carefully exposing and documenting the artifacts in their contexts, resulting in smaller horizontal and vertical exposures (e.g., Gilead, 2002). This typically hinders a fuller stratigraphic and contextual understanding of the excavated levels and features. Even worse, the method almost always creates a biased sample of “coordinated artifacts”, in which the most important items for understanding spatial patterns and site-formation processes – the small ones – are underrepresented. Some projects run the danger of treating the coordinated artifacts as bearing more significance than the sieve-recovered items, preferentially studying the former with no real justification. Finally, the coordinated items often go unassigned to a volumetric unit, effectively disconnecting them from other types of data found in the same depositional contexts.  

The advantages of piece-plotting may, in some cases, offset the disadvantages. But what I find missing in the general discourse (certainly not in the recommended preprint) is the “theory” behind the seemingly technical act of 3-coordinate recording (Yeshurun, 2022). Being in effect a form of sampling, this practice needs a rethink about where and how to be applied; what depositional contexts justify it, and what the goals are. These questions should determine if all “visible” artifacts are plotted, or just an explicitly defined sample of them (e.g., elongated items above a certain length threshold, which should be more reliable for fabric analysis), or whether the circumstances do not actually justify it. In the latter case, researchers sometimes opt for using “virtual coordinates” within in each spatial unit (typically 0.5x0.5 m), essentially replicating the data that is generated by “real” coordinates and integrating the sieve-recovered items as well. In either case, Royer et al.’s (2023) solution for plotting and visualizing labeled points within intra-site space would indeed be an important addition to the archaeologists’ tool kits.

References cited 

Discamps, E., Bachellerie, F., Baillet, M. and Sitzia, L. (2019). The use of spatial taphonomy for interpreting Pleistocene palimpsests: an interdisciplinary approach to the Châtelperronian and carnivore occupations at Cassenade (Dordogne, France). Paleoanthropology 2019, 362–388. https://doi.org/10.4207/PA.2019.ART136

Gilead, I. (2002). Too many notes? Virtual recording of artifacts provenance. In: Niccolucci, F. (Ed.). Virtual Archaeology: Proceedings of the VAST Euroconference, Arezzo 24–25 November 2000. BAR International Series 1075, Archaeopress, Oxford, pp. 41–44.

Royer, A., Discamps, E., Plutniak, S. and Thomas, M. (2023). SEAHORS: Spatial Exploration of ArcHaeological Objects in R Shiny Zenodo, 7957154, ver. 2 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7929462

Yeshurun, R. (2022). Intra-site analysis of repeatedly occupied camps: Sacrificing “resolution” to get the story. In: Clark A.E., Gingerich J.A.M. (Eds.). Intrasite Spatial Analysis of Mobile and Semisedentary Peoples: Analytical Approaches to Reconstructing Occupation History. University of Utah Press, pp. 27–35.