Measuring and automated detection of archaeological features in LiDAR datasets
ORGANIZERS
Clément Laplaige
UMR7324 CITERES, Laboratoire Archéologie et Territoires, Université François-Rabelais de Tours/CNRS, France
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John Pouncett
Institute of Archaeology, Université of Oxford, UK
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Xavier Rodier
CNRS, UMR7324 CITERES, Laboratoire Archéologie et Territoires, Université François-Rabelais de Tours/CNRS, France
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Abstract
This session will be an opportunity to discuss the latest developments in automated detection and classification of archaeological features in LiDAR datasets, whether in the form of point clouds or digital terrain models. Algorithms for automated detection and classification of archaeological features in LiDAR data are increasingly being employed by archaeologists. They offer the potential to rapidly identify archaeological features in large datasets with limited manual intervention. However, the use of such methods presents new challenges and discriminatory processes must be put in place to deal with errors such as the identification of false positives. Automatic feature detection and classification requires the user to adequately define signatures based on the morphometric characteristics of features of archaeological interest in order to obtain a satisfactory detection rate.
