Call for papers for CAA 2025 in Athens

The call for abstracts for the CAA 2025 conference in Athens has begun. CAA members are co-organizing up to six sessions! We cordially invite you to submit abstracts by October 29, 2024:

https://2025.caaconference.org/call-for-papers/

List

Abstracts

S26: Bridging Non-Invasive and Invasive Archaeology. Developing Computational Tools for Integration, Archiving, Visualisation and Analysis of Multifaceted Datasets

Piotr Wroniecki
Piotr Wroniecki
Kamil Niedziółka
Kamil Niedziółka

Session Format: Standard

This session aims to present ideas, programs, and tools designed to advance the processing and application of non-invasive (geophysics, LiDAR, remote sensing, field-walking etc…) and invasive data in research and rescue archaeology. We focus on their integration into excavation planning, interpretation, and post-processing, with special emphasis on comparative analysis between various datasets. The session encourages contributions that explore:

  • Integrating diverse datasets into excavation planning, interpretation, and post-processing
  • Applying machine learning to archaeological data analysis and management
  • Developing systematic, quantitative methods to evaluate non-invasive techniques in archaeology
  • Proposing new methodologies for comparing excavation data with non-invasive datasets
  • Addressing challenges in managing and analyzing large-scale archaeological datasets
  • Statistical and numerical approaches for dataset comparison and integration
  • Machine learning applications in data sorting, retrieval, classification, and anomaly detection
  • Addressing challenges in managing and analyzing large-scale archaeological datasets
  • Innovative data presentation and visualization techniques

A key focus is to move beyond traditional visual and intuition-based assessments towards more statistical and numerical approaches in evaluating excavation data in relation to non-invasive datasets. We seek papers that propose new methodologies for scientifically comparing and integrating these diverse data types, offering efficient ways to gain insights into how they can enhance the archaeological process. As archaeology grapples with increasingly vast datasets generated by modern prospection and documentation techniques, we also welcome submissions addressing the challenges of data management. This includes exploring tools or solutions for efficient storage, categorization, and retrieval of large collections of digital imagery and other data types. Machine learning approaches to data sorting, retrieval, classification, and anomaly detection are of particular interest. Whether presenting practical completed projects or more theoretical forward-thinking concepts, this session aims to continue the discussion on data integration in archaeology. Our goal is to bridge the gap between non-invasive and invasive archaeological methods that exists often due to lack of concepts or tools that would help bridge this divide.

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S27: Release the Kraken – Mobile GIS empowering survey communities across the globe

Julia M. Chyla
Julia M. Chyla
Nazarij Buławka
Nazarij Buławka

Session Format: Other

Over the past decades, archaeological field surveys have significantly refined and adapted methodologies to suit various global contexts, from the Mediterranean and Near East to the Americas (Alcock and Cherry 2004; Bintliff, Howard, and Snodgrass 1999; Athanassopoulos and Wandsnider 2011; Banning 2002). In addition to the specific characteristics of each context, it is essential to reflect on the tools and techniques employed and how they are integrated into investigative methodologies. We can observe a gradual change from the site-oriented prospection into a more holistic approach, considering extended artifact scatters and the elusive remains of human presence in the landscape (Knodell et al. 2023). A significant focus was mapping the density of archaeological material between sites using systematic sampling or transects (Judge 1981; Binford 1975; Nance 1983).

The integration of platforms, tools, unmanned aerial vehicles (drones) and artificial intelligence (AI) allows for multi-scale analysis, producing significant results through the possibility to analyze both the quantitative and qualitative dimensions of archaeological data. These advancements enable researchers to better understand the relationship between different layers of information, leading to new insights into landscape archaeology and the complexities of past human-environment interactions. A pressing issue in archaeological surveys is the impact of intensive land use over time, which has led to the depletion of visible archaeological records on the surface. On the other hand, forested areas present unique challenges, as these environments—where archaeological remains might be better preserved—still lack optimized survey strategies (Mazzacca et al. 2022). As field conditions evolve, so too must our methodologies, adapting to account for both degraded landscapes and new technologies that open up a wide range of possibilities.

The GNSS technologies and the expansion of portable handheld devices led to the development of what is currently known as Mobile GIS (Tripcevich 2004; Chyla and Buławka 2020; Sobotkova et al. 2015). It can be used for personal or collaborative work such as recording, monitoring, management, field verification, reporting, and didactics, which empowered archaeologists and cultural heritage authorities (Tibesasa 2021; Anbaroğlu et al. 2020; Abbas et al. 2023). It also changed the surface survey workflows and made systematic sampling or transects simpler and more accessible.

This CAA Mobile GIS Special Interest Group meeting intends to create an environment for presenting and discussing the current projects. It focuses on the extended use of Mobile GIS integrated with other platforms and tools for mapping and monitoring sites, artifacts, and other types of tangible and intangible heritage. In this meeting, we aim to explore the following questions: What changes, benefits, or challenges has mobile technology brought to the overall field workflow? How has it impacted the data lifecycle and research publication or reporting process? How can Mobile GIS impact and empower communities of archaeologists from different countries and continents? How the Mobile GIS can protect heritage and mitigate its destruction or looting?

The session welcomes papers devoted to:

  • field surveys,
  • recording, monitoring, management, field verification and reporting using portable devices,
  • Mobile GIS,
  • settlement analysis,
  • citizen science,
  • surveying in the forest,
  • public archaeology.

Other Format Description

The session will contain 20-minute presentations and will be followed by a discussion block.

References

Abbas, Riza, Sitaram Toraskar, Sanjay Exambekar, Emilia Smagur, V Shobha, and Andrzej Romanowski. 2023. ‘Geoarchaeological Investigations in and around the Ancient Port Site of Nalasopara: A Preliminary Study’. Studies in India 3 (1): 53–90.

Alcock, Susan E., and John F. Cherry. 2004. Side-by-Side Survey: Comparative Regional Studies in the Mediterranean World. New York. Oxbow Books. http://books.google.com/books?id=8YmBAAAAMAAJ&pgis=1.

Anbaroğlu, B., İ. B. Coşkun, M. A. Brovelli, T. Obukhov, and S. Coetzee. 2020. ‘Educational Material Development on Mobile Spatial Data Collection Using Open Source Geospatial Technologies’. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020:221–27. https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-221-2020.

Athanassopoulos, Effie F., and LuAnn Wandsnide. 2011. ‘Mediterranean Landscape Archaeology Past and Present’. In Mediterranean Archaeological Landscapes: Current Issues, edited by Effie F. Athanassopoulos and Luann Wandsnider, 1–14. Philadelphia, PA: University of Pennsylvania Press, Inc.

Banning, Edward B. 2002. Archaeological Survey. New York: Kluwer Academic Press.

Binford, Lewis R. 1975. ‘Sampling, Judgement, and the Archaeological Record’. In Sampling in Archaeology, edited by W. J. Mueller, 251–57. Tucson: University of Arizona Press.

Bintliff, John L., Phil Howard, and Anthony Snodgrass. 1999. ‘The Hidden Landscape of Prehistoric Greece’. Journal of Mediterranean Archaeology 12 (2): 139–68.

Chyla, Julia Maria, and Nazarij Buławka. 2020. ‘Mobile GIS – Current Possibilities, Future Needs. Position Paper’. In Digital Archaeologies, Material Worlds (Past and Present). Proceedings of the 45th Annual Conference on Computer Applications and Quantitative Methods in Archaeology, edited by Jeffrey B. Glover, Jessica Moss, and Dominique Rissolo, 99–113. Tübingen: Tübingen University Press. https://doi.org/10.15496/publikation-43226.

Judge, W.J. 1981. ‘Transect Sampling in Chaco Canyon – Evaluation of a Survey Technique’. In Archaeological Surveys of Chaco Canyon, New Mexico, edited by Alden C. Hayes, David M. Brugge, and James W. Judge, 107–37. Publications in Archaeology, 18A. Washington, D.C: U.S. Department of the Interior, National Park Service.

Knodell, Alex R., Toby C. Wilkinson, Thomas P. Leppard, and Hector A. Orengo. 2023. ‘Survey Archaeology in the Mediterranean World: Regional Traditions and Contributions to Long-Term History’. Journal of Archaeological Research 31 (2): 263–329. https://doi.org/10.1007/s10814-022-09175-7.

Mazzacca, G., Grilli, E., Cirigliano, G. P., Remondino, F., & Campana, S. (2022). Seeing among foliage with LIDAR and machine learning: towards a transferable archaeological pipeline; International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,46, 365-372.

Nance, Jack D. 1983. ‘Regional Sampling in Archaeological Survey: The Statistical Perspective’. Advances in Archaeological Method and Theory 6:289–356.

Sobotkova, Adela, Brian Ballsun-Stanton, Shawn Ross, and Penny Crook. 2015. ‘Arbitrary Offline Data Capture on All of Your Androids: The FAIMS Mobile Platform’. In Across Space and Time : Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology : Perth, 25-28 March 2013, 80–88. Amsterdam: Amsterdam University Press. http://en.aup.nl/books/9789089647153-across-space-and-time.html.

Tibesasa, Ruth. 2021. ‘An Archaeological Study of Farming Communities on the Northern Shores of Lake Victoria Nyanza, Uganda’. Doctoral thesis, Pretoria: University of Pretoria (South Africa).

Tripcevich, Nicholas. 2004. ‘Flexibility by Design: How Mobile GIS Meets the Needs of Archaeological Survey’. Cartography and Geographic Information Science 31 (3): 137–51.

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S28: Follow Rivers: the application of advanced remote sensing, machine learning and modelling in the studies of water management of past societies

Nazarij Buławka
Nazarij Buławka

Session Format: Standard

Studies related to ancient water management are particularly relevant to modern environmental problems and are central to the discourse of complex societies. Wittfogel’s hydraulic hypothesis (1955; 1957) provoked early archaeological studies to investigate the link between extensive irrigation systems and the centralised authority of early states. While present-day research emphasises the significance of water management, it also indicates a more complicated picture (Rost 2022; Wilkinson, Rayne, and Jotheri 2015). Mesopotamian examples indicate that irrigation developed gradually from cleaning parts of the crevasse splay into an extensive network of canals (Wilkinson and Hritz 2013). On the other hand, in the Indus Civilisation, irrigation was not necessary because agriculture was based on the monsoon cycle (Madella and Lancelotti 2022), while in southern Turkmenistan (Central Asia) canals for irrigation were already in place during the Chalcolithic period (Lisitsina 1969).

Water management is a highly complex research field requiring collaboration between different disciplines and the use of various methods. The irrigation landscape is palimpsest, and it can consist of canals, qanats, natural or partially modified channels, rivers, and streams from different periods (Jotheri 2018). The ancient landscape continuously evolves through human agency and natural processes, which leads to deleting or masking of the features (Wilkinson 2003). Computational methods, specifically when combined with other techniques in landscape archaeology, allow us to understand some of that complexity (Garcia et al. 2019). Recent computational method developments have changed how we study ancient landscapes. The appearance of a vast battery of high-resolution satellite images, including HEXAGON (Hammer, FitzPatrick, and Ur 2022), drone imagery (Campana 2017), newly available digital surface models (González et al. 2020), and large cloud datasets available in Google Earth Engine (Orengo and Petrie 2017), opened avenues for reconstructing irrigation systems with greater temporal and spatial resolution. Predictive or agent-based modelling offers another option to build hypotheses on past land use (Angourakis et al. 2014). While machine learning and deep learning provide much for the study, their application is still limited (Li et al. 2022). The session aims to bring together researchers attempting novel approaches in water management studies using computational methods. It welcomes papers focused on satellite remote sensing, Google Earth Engine, machine learning and deep learning, predictive or agent-based modelling of irrigation systems, detection or spatial analysis, and landscape evolution.

References

Angourakis, Andreas, Bernardo Rondelli, Sebastian Stride, Xavier Rubio-Campillo, Andrea L. Balbo, Alexis Torrano, Verònica Martinez, Marco Madella, and Josep M. Gurt. 2014. ‘Land Use Patterns in Central Asia. Step 1: The Musical Chairs Model’. Journal of Archaeological Method and Theory 21 (2): 405–25. https://doi.org/10.1007/s10816-013-9197-0.

Campana, Stefano. 2017. ‘Drones in Archaeology. State-of-the-Art and Future Perspectives’. Archaeological Prospection 24 (4). https://doi.org/10.1002/arp.1569.

Garcia, Arnau, Hector Orengo, Francesc Conesa, Adam Green, and Cameron Petrie. 2019. ‘Remote Sensing and Historical Morphodynamics of Alluvial Plains. The 1909 Indus Flood and the City of Dera Ghazi Khan (Province of Punjab, Pakistan)’. Geosciences 9 (1): 21. https://doi.org/10.3390/geosciences9010021.

González, Carolina, Markus Bachmann, José-Luis Bueso-Bello, Paola Rizzoli, and Manfred Zink. 2020. ‘A Fully Automatic Algorithm for Editing the TanDEM-X Global DEM’. Remote Sensing 12 (23): 3961. https://doi.org/10.3390/rs12233961.

Hammer, Emily, Mackinley FitzPatrick, and Jason Ur. 2022. ‘Succeeding CORONA: Declassified HEXAGON Intelligence Imagery for Archaeological and Historical Research’. Antiquity 96 (387): 679–95.

Jotheri, Jaafar. 2018. ‘Recognition Criteria for Canals and Rivers in the Mesopotamian Floodplain’. In Water Societies and Technologies from the Past and Present, edited by Yijie Zhuang and Mark Altaweel, 111–26. UCL Press. https://doi.org/10.2307/j.ctv550c6p.12.

Li, Qian, Huadong Guo, Lei Luo, and Xinyuan Wang. 2022. ‘Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model’. Remote Sensing 14 (14): 3318. https://doi.org/10.3390/rs14143318.

Lisitsina, Gorislava Nikolaevna. 1969. ‘The Earliest Irrigation in Turkmenia’. Antiquity 43 (172): 279–88, pl. XXXIX.

Madella, Marco, and Carla Lancelotti. 2022. ‘Archaeobotanical Perspectives on Water Supply and Water Management in the Indus Valley Civilization’. In Irrigation in Early States New Directions, 113–36. Chicago: Oriental Institute of the University of Chicago.

Rost, Stephanie. 2022. ‘Introduction’. In Irrigation in Early States New Directions, edited by Stephanie Rost, xi–xxx. Chicago: Oriental Institute of the University of Chicago.

Wilkinson, Tony James. 2003. ‘Landscape of Irrigation’. In Archaeological Landscapes of the Near East, 71–99. Tucson: University of Arizona Press.

Wilkinson, Tony James, and Carrie Hritz. 2013. ‘Physical Geography, Environmental Change and the Role of Water’. In Models of Mesopotamian Landscapes: How Small-Scale Processes Contributed to the Growth of Early Civilizations, 1–34. Archaeopress Oxford, UK.

Wilkinson, Tony James, Louise Rayne, and Jaafar Jotheri. 2015. ‘Hydraulic Landscapes in Mesopotamia: The Role of Human Niche Construction’. Water History 7 (4): 397–418. https://doi.org/10.1007/s12685-015-0127-9.

Wittfogel, Karl August. 1955. ‘Developmental Aspects of Hydraulic Societies’. Irrigation Civilizations: A Comparative Study, 43–53.

———. 1957. Oriental Despotism; a Comparative Study of Total Power. New Haven: Yale University Press.

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S29: Heritage under bombs – digital methods in the studies of endangered heritage in conflict zones

Nazarij Buławka
Nazarij Buławka

Session Format: Other

In the recent few years, the political situation across Ukraine, Sudan, Syria, Levant, Central African Republic, Afghanistan, Manipur (India), Georgia, and many other places in the globe poses a critical threat to the preservation of tangible and intangible heritage (Shydlovskyi et al. 2023; Shydlovskyi, Telizhenko, and Ivakin 2023; Ahmad 2022). This includes destruction, bombing, usage for military activity or looting. Among the most recent examples, the war in Ukraine and the Middle East crisis show that heritage became not only a silent victim of conflict but also a tool for whitewashing military actions towards civilians. Looted artifacts, in turn, often end up on the market, and the revenue helps to finance military operations. In early September 2024, shortly before the submission of the session abstract, artifacts from Sudanese museums began appearing on online auctions.

A basic principle in the practice of cultural resource management is that to be effective in protecting and managing any kind of heritage (from small objects to buildings, landscapes and including intangible cultural heritage), knowing what heritage you have is essential to safeguarding it. Even though various institution-led or community-based actions are being taken to document heritage before and after it is destroyed, unfortunately, it vanishes faster than any archaeologist or museologist can work. Therefore, there is a deep necessity for creating and sharing a common methodology for protecting and conserving heritage through digital methods and the ways such digital skills can be transferred/shared with institutions and professionals in regions affected by conflicts. This session intends to bring together researchers from areas affected by conflict and war, specialized in digital methods, and deeply concerned about the future of heritage. The session welcomes papers devoted to monitoring archaeological heritage in conflict zones focused on:

  • inventories and database for heritage preservation;
  • remote sensing (from satellite to unmanned aerial vehicles UAV):
  • photogrammetry and laser scanning;
  • citizen science.

Other Format Description

The session will include regular presentations and a more extended discussion block at the end.

References

Bevan, R. 2016. The destruction of memory: architecture at war. London: Reaktion.

Campana, S., Sordini, M., Berlioz, S., Vidale, M., Al-Lyla, R., Abbo al-Araj, A., & Bianchi, A. 2022. Remote sensing and ground survey of archaeological damage and destruction at Nineveh during the ISIS occupation. Antiquity, 1-19, https://doi.org/10.15184/aqy.2022.14

 Casana J., 2015. The Cultural Heritage Crisis in the Middle East, Vol. 78, No. 3, Special Issue. Casana, J. & E.J. Laugier. 2017. Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war. PLoS ONE 12: 1–31. https://doi.org/10.1371/journal.pone.0188589

Newson P., Young R., 2018. Post-conflict archaeology and cultural heritage rebuilding knowledge, memory and community from war-damaged material culture, New York, Routledge.

 Ahmad, Rukhsar. 2022. ‘The Legal Role of Government in Protecting Cultural Heritage and Archaeological Sites in the War-Affected Countries: The Case of Iraq and Syria’. Journal of Liberty and International Affairs 8 (2): 281–92.

Shydlovskyi, Pavlo S., Ian Kuijt, Viacheslav Skorokhod, Ivan Zotsenko, Vsevolod Ivakin, William Donaruma, and Sean Field. 2023. ‘The Tools of War: Conflict and the Destruction of Ukrainian Cultural Heritage’. Antiquity 97 (396): e36. https://doi.org/10.15184/aqy.2023.159.

Shydlovskyi, Pavlo S., Serhii A. Telizhenko, and Vsevolod H. Ivakin. 2023. ‘Archaeological Monitoring in War-Torn Ukraine’. The Historic Environment: Policy & Practice 14 (2): 154–80. https://doi.org/10.1080/17567505.2023.2209835.

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S37: Computational and Landscape Archaeology in the renovation of surface survey methodologies

Nazarij Buławka
Nazarij Buławka

Session Format: Standard

Remains of artefacts, architecture and other features visible on the earth surface are one of the main instruments for archaeologists to understand how past human populations inhabited and transformed the environment. Beyond the more traditional objective of localizing the best sites to excavate, surface record can be exploited by itself for the analysis of past cultural phenomena at both local and/or regional levels.

In the study of this complex surface record, the development of geospatial conceptual frameworks, methods and technologies played a central role in how archaeologists record field data and analyse the resulting datasets (Wheatley and Gillings 2013). One outstanding example of this integration can be traced since the introduction of the concept of systematic pedestrian survey at regional-scale, with the measuring of pottery scatters as its main target. This approach has a strong development in Eastern Mediterranean (Knodell et al. 2023), and in Greece in particular, since the 1950s, with multiple projects active nowadays, some of them with a long tradition (Bintliff et al. 1999; Alock and Cherry 2004). Its historical development has been parallel an intertwined with the development of geospatial technologies, and GIS in particular.

At the same time, the use of aerial and then satellite imagery changed the way surveys were done. First, the application of aerial and satellite imagery along with geophysics allowed the mapping of the structures visible at the sites (Campana and Piro 2009). Secondly, conducting remote sensing research allowed the mapping of countless sites for field verification (Casana 2014; Banning 2002, 136). In recent years, there has been an accumulation of developments on geospatial technologies that are being tested in the context of archaeological survey workflows, followed by a process of integration in the common practices of survey teams.

The availability of geospatial data has been exponentially increasing: multi-temporal sequences of aerial imagery, high-resolution orthomaps, multi-spectral and radar datasets, and digitised collection of archival photographic and cartographic datasets, are just some examples. This have been accompanied with the creation of specific platforms and software that allow the processing of this enormous geospatial information. The increasing extended use of Machine-Learning based approaches in archaeology is having a strong impact, which can be tracked in recent CAA and other international meetings. Some researches have use ML and DL algorithms to assist in the mapping of features such artificial mounds (Menze & Ur 2012; Berganzo-Besga et al. 2021 & 2023; Garcia-Molsosa et al. 2021) or hydraulic infrastructure (Bulawka et al. 2024 a&b).

The appearance of unmanned aerial vehicles (drones) has increased the resolution of captured imagery (Campana 2017), and given the archaeologists the capacity of capture information at the scale of specific archaeological features in large areas, something that was very costly until now, which limited works of Remote Sensing only to the study of large features that could be visible in large-scale images. Until recently, for example, it was not possible to use remote sensing to focus on the artifacts themselves. Thanks to the application of machine / deep learning with remote sensing, an opportunity for large-scale mapping pottery appeared (Orengo et al. 2021).

As this scale gap closes, the integration of multiple datasets, scales, techniques and sources in survey workflows, puts archaeological survey in front of a potential new step. In this session, we are inviting researchers interested in sharing how they are incorporating this new geospatial technologies to their surface surveys, and to discuss the current state of the art, and future perspectives for the application of computational methods in archaeological surveys.

At this session we would like to welcome every research on computational archaeology applied to field survey and the interpretation of surface datasets, in particular:

  • Theoretical and conceptual approaches to archaeological surface record.
  • Remote and on-field recording practices and the creation of archaeological geodatabases.
  • Automatised mapping of archaeological features, including artefacts, structures and landforms.
  • Statistics, computing modelling and other analytical methods applied to survey datasets.
  • Geophysics integration on archaeological surveys.
  • Design and development of regional surveys.

References

Alcock, S. E., and Cherry, J. F. (2004). Introduction. In Alcock, S. E., and Cherry, J. F. (eds.), Side-by Side Survey: Comparative Regional Studies in the Mediterranean World, Oxbow, Oxford, pp. 1–9.

Banning, Edward B. 2002. Archaeological Survey. New York: Kluwer Academic Press.

Bintliff, John L., Phil Howard, and Anthony Snodgrass. 1999. ‘The Hidden Landscape of Prehistoric Greece’. Journal of Mediterranean Archaeology 12 (2): 139–68.

Buławka, N.; Orengo, H.A. 2024a. Application of Multi-Temporal and Multisource Satellite Imagery in the Study of Irrigated Landscapes in Arid Climates. Remote Sens. 16, 1997.

Buławka, N.; Orengo, H. A.; Berganzo-Besga, I. 2024 Deep learning-based detection of qanat underground water distribution systems using HEXAGON spy satellite imagery, Journal of Archaeological Science, Volume 171, 106053, https://doi.org/10.1016/j.jas.2024.106053

Berganzo-Besga, I., Orengo, H. A., Lumbreras, F., Carrero-Pazos, M., Fonte, J., & Vilas-Estévez, B. (2021). Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia. Remote Sensing (20), Article 20. https://doi.org/10.3390/rs13204181

Berganzo-Besga, I., Orengo, H. A., Lumbreras, F., Alam, A., Campbell, R., Gerrits, P. J., de Souza, J. G., Khan, A., Suárez-Moreno, M., Tomaney, J., Roberts, R. C., & Petrie, C. A. (2023). Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan. Scientific Reports 13 (1), Article 1. https://doi.org/10.1038/s41598-023-38190-x

Campana, Stefano. 2017. ‘Drones in Archaeology. State-of-the-Art and Future Perspectives’. Archaeological Prospection 24 (4). https://doi.org/10.1002/arp.1569.

Campana, Stefano, and Salvatore Piro. 2009. Seeing the Unseen: Geophysics and Landscape Archeology. Boca Raton, London, New York, Leiden: CRC Press of Taylor & Francis Group. https://doi.org/10.1002/arp.365

Casana, Jesse. 2014. ‘Regional-Scale Archaeological Remote Sensing in the Age of Big Data’. Advances in Archaeological Practice 2 (03): 222–33. https://doi.org/10.7183/2326-3768.2.3.222

Garcia-Molsosa, A., Orengo, H. A., Lawrence, D., Philip, G., Hopper, K., & Petrie, C. A. (2021). Potential of deep learning segmentation for the extraction of archaeological features from historical map series. Archaeological Prospection 28 (2), 187–199. https://doi.org/10.1002/arp.1807

Knodell, Alex R., Toby C. Wilkinson, Thomas P. Leppard, and Hector A. Orengo. 2023. ‘Survey Archaeology in the Mediterranean World: Regional Traditions and Contributions to Long-Term History’. Journal of Archaeological Research 31 (2): 263–329. https://doi.org/10.1007/s10814-022-09175-7h

Menze, B. H., & Ur, J. A. (2012). Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale. Proceedings of the National Academy of Sciences 109(14), E778–E787. https://doi.org/10.1073/pnas.1115472109

Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A. (2020). Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data. Proceedings of the National Academy of Sciences 117( 31), 18240–18250. https://doi.org/10.1073/pnas.2005583117

Orengo, Hector A., Arnau Garcia-Molsosa, Iban Berganzo-Besga, Juergen Landauer, Paloma Aliende, and Sergi Tres-Martínez. 2021. ‘New Developments in Drone-Based Automated Surface Survey: Towards a Functional and Effective Survey System’. Archaeological Prospection, no. November 2020, 1–8. https://doi.org/10.1002/arp.1822.

Wheatley, D., & Gillings, M. (2013). Spatial Technology and Archaeology: The Archaeological Applications of GIS. CRC Press.

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S51: Bridging the gap between theory and practice: Teaching digital fieldwork archaeology

Paweł Lech
Paweł Lech
Łukasz Miszk
Łukasz Miszk

Session Format: Standard

Digital archaeology has revolutionized the way to conduct fieldwork offering innovative methods for survey, documentation, analysis, and the preservation of archaeological sites. Incorporating digital tools into archaeological fieldwork has allowed for greater precision, efficiency, and enhanced data interpretation. As we move into an era where technology is integral to cultural heritage protection, it is crucial to equip the next generation of archaeologists with the skills and knowledge needed to use these advanced methods effectively. This session proposal seeks to bring together experts in digital archaeology to explore and exchange knowledge about the teaching of modern fieldwork methods. The focus will be on integrating a wide range of technologies into the practical training of students and professionals alike, covering geophysical prospection, remote sensing, 3D documentation, excavations, and digital documentation through GIS and WebGIS platforms. Key themes include expanding the digital horizons of fieldwork, applying non-invasive techniques in archaeological research, and promoting digital methods for heritage protection. By equipping students with these skills, we ensure they are well-prepared to navigate the increasingly digital landscape of archaeology, fostering a new generation of archaeologists adept at using cutting-edge technologies to preserve and interpret the past. Despite the success of digital archaeology training programs, several challenges can arise during field schools and seminars that must be addressed. One of the most significant difficulties is the complexity of the technology itself. Operating advanced geophysical equipment or UAVs requires a steep learning curve, and without proper instruction, students may struggle to collect high-quality data. Moreover, the interpretation of geophysical and remote sensing data can be daunting for those unfamiliar with the software and analytical processes. This session would like to invite papers that include a variety of teaching approaches and encourage a discussion on best practices in the field with a focus on the sustainable transfer of knowledge for archaeologists.

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