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The data explosion: tackling the taboo of automatic feature recognition in airborne survey data

Published online by Cambridge University Press:  26 August 2014

Rebecca Bennett ,
Dave Cowley  and
Véronique De Laet
Rebecca Bennett
Affiliation:
1Department of Archaeology, University of Winchester, King Alfred Campus, Sparkford Road, Winchester SO22 4NR, UK
Dave Cowley
Affiliation:
2Royal Commission on the Ancient and Historical Monuments of Scotland, John Sinclair House, 16 Bernard Terrace, Edinburgh EH8 9NX, UK
Véronique De Laet
Affiliation:
3Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, B-3001 Leuven-Heverlee, Belgium 43GeoID, Researchpark Haasrode, Interleuvenlaan 62, 3001 Leuven, Belgium
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Abstract

The increasing availability of multi-dimensional remote-sensing data covering large geographical areas is generating a new wave of landscape-scale research that promises to be as revolutionary as the application of aerial photographic survey during the twentieth century. Data are becoming available to historic environment professionals at higher resolution, greater frequency of acquisition and lower cost than ever before. To take advantage of this explosion of data, however, a paradigm change is needed in the methods used routinely to evaluate aerial imagery and interpret archaeological evidence. Central to this is a fuller engagement with computer-aided methods of feature detection as a viable way to analyse airborne and satellite data. Embracing the new generation of vast datasets requires reassessment of established workflows and greater understanding of the different types of information that may be generated using computer-aided methods.

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Antiquity , Volume 88 , Issue 341 , September 2014 , pp. 896 - 905
Copyright
Copyright © Antiquity Publications Ltd 2014

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References

Beck, A. 2011. Archaeological applications of multi/hyper-spectral data—challenges and potential, in Cowley, D.C. (ed.) Remote sensing for archaeological heritage management (Europae Archaeologiae Consilium Occasional Papers 5): 87–97. Budapest: Archaeolingua.Google Scholar
Bennett, R., Welham, K., Hill, R.A. & Ford, A.. 2011. Making the most of airborne remote sensing techniques for archaeological survey and interpretation, in Cowley, D.C. (ed.) Remote sensing for archaeological heritage management (Europae Archaeologiae Consilium Occasional Papers 5): 99–106. Budapest: Archaeolingua.Google Scholar
Bennett, R., Welham, K., Hill, R.A. & Ford, A. 2012. A comparison of visualization techniques for models created from airborne laser scanned data. Archaeological Prospection 19: 41–48. http://dx.doi.org/10.1002/arp.1414CrossRefGoogle Scholar
Brophy, K. & Cowley, D.. 2005. From the air—understanding aerial archaeology. Stroud: Tempus.Google Scholar
Cowley, D.C. 2011. Remote sensing for European archaeology and heritage management—site discovery, interpretation and registration, in Cowley, D.C. (ed.) Remote sensing for archaeological heritage management (Europae Archaeologiae Consilium Occasional Papers 5): 43–55. Budapest: Archaeolingua.Google Scholar
Cowley, D.C. & Huld, K. Sigur−dard´ottir. 2011. Remote sensing for archaeological heritage management, in Cowley, D.C. (ed.) Remote sensing for archaeological heritage management (Europae Archaeologiae Consilium Occasional Papers 5): 11–16. Budapest: Archaeolingua.Google Scholar
Cowley, D.C., Laet, V. De & Bennett, R.A.. 2013. Auto-extraction techniques and cultural heritage databases, in Neubauer, W., Trinks, I., Salisbury, R. & Einwögerer, C. (ed.) Proceedings of the 10th International Conference on Archaeological Prospection, Vienna, May 29–June 2 2013: 406–408. Vienna: Ludwig Boltzmann Institute.10.2307/j.ctvjsf630.153CrossRefGoogle Scholar
Domingos, P. 2012. A few useful things to know about machine learning. Communications of the ACM 55(10): 78–87. http://dx.doi.org/10.1145/2347736.2347755CrossRefGoogle Scholar
Duckers, G.L. 2013. Bridging the ‘geospatial divide’ in archaeology: community based interpretation of LIDAR data. Internet Archaeology 35. http://dx.doi.org/10.11141/ia.35.10Google Scholar
Gojda, M. 2011. Remote sensing for the integrated study and management of sites and monuments—a Central European perspective and Czech case study, in Cowley, D.C. (ed.) Remote sensing for archaeological heritage management (Europae Archaeologiae Consilium Occasional Papers 5): 215–34. Budapest: Archaeolingua.Google Scholar
Grøn, O., Palmér, S., Stylegar, F., Esbensen, K., Kucheryavski, S. & Aase, S.. 2011. Interpretation of archaeological small-scale features in spectral images. Journal of Archaeological Science 38: 2024–30. http://dx.doi.org/10.1016/j.jas.2009.11.023CrossRefGoogle Scholar
Halliday, S. 2013. I walked, I saw, I surveyed, but what did I see?…and what did I survey? in Opitz, R. & Cowley, D.C. (ed.) Interpreting archaeological topography: lasers, 3D data, observation, visualisation and applications: 63–75. Oxford: Oxbow.10.2307/j.ctvh1dqdz.10CrossRefGoogle Scholar
Hanson, W.S. 2010. The future of aerial archaeology in Europe. Photo Interprétation: European Journal of Applied Remote Sensing 46(1): 3–11.Google Scholar
Hill, R. 2009. The roar of the butterflies. London: HarperCollins.Google Scholar
Horne, P. 2009. A strategy for the National Mapping Programme. Swindon: English Heritage.Google Scholar
De Laet, V., Paulissen, E. & Waelkens, M.. 2007. Methods for the extraction of archaeological features from very high-resolution Ikonos-2 remote sensing imagery, Hisar (southwest Turkey). Journal of Archaeological Science 34: 830–41. http://dx.doi.org/10.1016/j.jas.2006.09.013CrossRefGoogle Scholar
Lambers, K. & Zingman, I.. 2013. Texture segmentation as a first step towards archaeological object detection in high resolution satellite images of the Silvretta Alps, in Neubauer, W., Trinks, I., Salisbury, R. & Einwögerer, C. (ed.) Proceedings of the 10th International Conference on Archaeological Prospection, Vienna May 29–June 2 2013: 327–29. Vienna: Ludwig Boltzmann Institute.10.2307/j.ctvjsf630.124CrossRefGoogle Scholar
Lasaponara, R. & Masini, N.. 2012. Satellite remote sensing: a new tool for archaeology. New York: Springer. http://dx.doi.org/10.1007/978-90-481-8801-7CrossRefGoogle Scholar
Palmer, R. 2011. Knowledge-based aerial image interpretation, in Cowley, D.C. (ed.) Remote sensing for archaeological heritage management (Europae Archaeologiae Consilium Occasional Papers 5): 283–91. Budapest: Archaeolingua.Google Scholar
Parcak, S. 2009. Satellite remote sensing for archaeology. London: Routledge.10.4324/9780203881460CrossRefGoogle Scholar
Pascal & Pascal2. 2013. Pattern analysis, statistical modelling and computational learning. Available at: http://pascallin.ecs.soton.ac.uk/ and http://pascallin2.ecs.soton.ac.uk/ (accessed 17 June 2014).Google Scholar
Risbøl, O., Bollands°as, O.M., nesbakken, A., Ørka, H., Næsset, E. & Gobakken, T.. 2013. Interpreting cultural remains in airborne laser scanning generated digital terrain models: effects of size and shape on detection success rates. Journal of Archaeological Science 40: 4688–700. http://dx.doi.org/10.1016/j.jas.2013.07.002CrossRefGoogle Scholar
Sonka, M., Hlavac, V. & Boyle, R.. 2008. Image processing, analysis and machine vision. Toronto: Thomson Learning.Google Scholar
Trier, Ø. & Pilø, L.. 2012. Automatic detection of pit structures in airborne laser scanning data. Archaeological Prospection 19: 103–21. http://dx.doi.org/10.1002/arp.1421CrossRefGoogle Scholar
Trier, Ø., Larsen, S. & Solberg, R.. 2009. Automatic detection of circular structures in high-resolution satellite images of agricultural land. Archaeological Prospection 16: 1–15. http://dx.doi.org/10.1002/arp.339CrossRefGoogle Scholar
Verhagen, P. & Dr˘agut¸, L.. 2012. Object-based landform delineation and classification from DEMs for archaeological predictive mapping. Journal of Archaeological Science 39: 698–703. http://dx.doi.org/10.1016/j.jas.2011.11.001CrossRefGoogle Scholar
Verhoeven, G. 2012. Near-infrared aerial crop mark archaeology: from its historical use to current digital implementations. Journal of Archaeological Method and Theory 19: 132–60. http://dx.doi.org/10.1007/s10816-011-9104-5CrossRefGoogle Scholar
Wilson, D.R. 2000. Air photo interpretation for archaeologists. London: Tempus.Google Scholar