Volume I-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-2, 189-194, 2012
https://doi.org/10.5194/isprsannals-I-2-189-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-2, 189-194, 2012
https://doi.org/10.5194/isprsannals-I-2-189-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  13 Jul 2012

13 Jul 2012

MODELLING VAGUE KNOWLEDGE FOR DECISION SUPPORT IN PLANNING ARCHAEOLOGICAL PROSPECTIONS

S. Boos1, S. Hornung2, and H. Müller1 S. Boos et al.
  • 1Mainz University of Applied Sciences, Institute for Spatial Information and Surveying Technology (i3mainz), Lucy-Hillebrand-Str. 2, D-55128 Mainz, Germany
  • 2Mainz University, Institute for Pre- and Protohistory, Schillerstr. 11, D-55116 Mainz, Germany

Keywords: archaeology, GIS, prediction, modelling, visualization, parameters, decision support, landscape, spatial

Abstract. Most archaeological predictive models lack significance because fuzziness of data and uncertainty in knowledge about human behaviour and natural processes are hardly ever considered. One possibility to cope with such uncertainties is utilization of probability based approaches like Bayes Theorem or Dempster-Shafer-Theory. We analyzed an area of 50 km2 in Rhineland Palatinate (Germany) near a Celtic oppidum by use of Dempster-Shafer's theory of evidence for predicting spatial probability distribution of archaeological sites. This technique incorporates uncertainty by assigning various weights of evidence to defined variables, in that way estimating the probability for supporting a specific hypothesis (in our case the hypothesis presence or absence of a site). Selection of variables for our model relied both on assumptions about settlement patterns and on statistically tested relationships between known archaeological sites and environmental factors. The modelling process was conducted in a Geographic Information System (GIS) by generating raster-based likelihood surfaces. The corresponding likelihood surfaces were aggregated to a final weight of evidence surface, which resulted in a likelihood value for every single cell of being a site or a non-site. Finally the result was tested against a database of known archaeological sites for evaluating the gain of the model. For the purpose of enhancing the gain of our model and sharpening our criteria we used a two-step approach to improve the modelling of former settlement strategies in our study area. Applying the developed model finally yielded a 100 percent success rate of known archaeological sites located in predicted high potential areas.