Abstract
Chronological modeling is part of a methodology being developed for the use of Airborne Laser Scanning (ALS) data in reconstructing historical road and path networks in vegetated areas. It comprises four main steps. The first step tackles the (semi-) automatic visualization and extraction of linear features from ALS data. A model is presented in the second step to determine the (relative) chronology of historical roads and paths. The third step deals with the predictive modeling of unknown networks. The final step combines a 3-D environment with a time element, resulting in a temporal-spatial model of the road and path networks found. The (semi-) automatic extraction results of the first step are published in August 2014 in the proceedings of the Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014). Based on these extracted networks of the first step and additional manual mapping, the chronological model of the second step is created. The outcome of the model will be presented in this article. The chronological model makes use of both the Harris Matrix Composer (HMC) and the Online Cultural and Historical Research Environment (OCHRE). The latter is developed at the University of Chicago. This is a multi-project, multi-user database system that provides a comprehensive framework for diverse kinds of information at all stages of research. Both the HMC and OCHRE proved useful for creating a relative dating model for roads and paths, as resulted from the case study presented in this article.
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Acknowledgments
I would like to thank Eamonn Baldwin of the University of Birmingham for his critical look at the first drafts and the staff of the OCHRE Data Service of the University of Chicago (http://ochre.uchicago.edu) for assistance and for providing complimentary student use of the OCHRE software.
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Vletter, W.F., Schloen, S.R. (2016). Creating a Chronological Model for Historical Roads and Paths Extracted from Airborne Laser Scanning Data. In: Forte, M., Campana, S. (eds) Digital Methods and Remote Sensing in Archaeology. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-40658-9_18
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