5 August 2013 GIS and archaeology: a spatial predictive model for neolithic sites of the Tavoliere (Apulia)
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Proceedings Volume 8795, First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013); 87950I (2013) https://doi.org/10.1117/12.2027954
Event: First International Conference on Remote Sensing and Geoinformation of Environment, 2013, Paphos, Cyprus
Abstract
Two are the risks associated to archaeological heritage. The first one is economic and it is related to the costs needed to perform field survey in all the territory. The second one is scientific, that is the risk to lose artifacts and, consequently, witnesses of the human past [1], in particular in case of large-scale infrastructure works and looting linked to the illicit trade of antiquities. Predictive models are useful to archaeological research to look for the right compromise between the reduction of costs and effectiveness of results. It enables to identify the site locational behavior [2], more in particular environmental site location preferences [3]. From the eighties the development of more user friendly GIS softwares and the increased easiness of relative tools allowed the diffusion of predictive models, that were improved thanks to the greater availability of remotely sensed data and the image processing routines, which are effective for the detection of archaeological features. The proposal of this paper is to make a brief review of existing predictive models and to propose a new model that takes in count spatial properties of archaeological datasets to predict neolithic settlements in Tavoliere in the Apulian region (Southern Italy), already investigated by preventive archaeological methods including geophysics and remote sensing [4].
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Maria Danese, Maria Danese, Nicola Masini, Nicola Masini, Marilisa Biscione, Marilisa Biscione, Rosa Lasaponara, Rosa Lasaponara, } "GIS and archaeology: a spatial predictive model for neolithic sites of the Tavoliere (Apulia)", Proc. SPIE 8795, First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013), 87950I (5 August 2013); doi: 10.1117/12.2027954; https://doi.org/10.1117/12.2027954
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