ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W3, 61-68, 2015
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5-W3/61/2015/
doi:10.5194/isprsannals-II-5-W3-61-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
11 Aug 2015
5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources
A. Doulamis1, N. Doulamis1, C. Ioannidis1, C. Chrysouli2, N. Grammalidis2, K. Dimitropoulos2, C. Potsiou1, E.-K. Stathopoulou1, and M. Ioannides3 1National Technical University of Athens, 9 Iroon Polytechniou St., Athens, Greece
2Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
3Cyprus University of Technology, Limassol, Cyprus
Keywords: Large scale Cultural Heritage sites, Cultural Heritage Model, 5D modelling, Selective modelling, CityGML, Visualization Abstract. Outdoor large-scale cultural sites are mostly sensitive to environmental, natural and human made factors, implying an imminent need for a spatio-temporal assessment to identify regions of potential cultural interest (material degradation, structuring, conservation). On the other hand, in Cultural Heritage research quite different actors are involved (archaeologists, curators, conservators, simple users) each of diverse needs. All these statements advocate that a 5D modelling (3D geometry plus time plus levels of details) is ideally required for preservation and assessment of outdoor large scale cultural sites, which is currently implemented as a simple aggregation of 3D digital models at different time and levels of details. The main bottleneck of such an approach is its complexity, making 5D modelling impossible to be validated in real life conditions. In this paper, a cost effective and affordable framework for 5D modelling is proposed based on a spatial-temporal dependent aggregation of 3D digital models, by incorporating a predictive assessment procedure to indicate which regions (surfaces) of an object should be reconstructed at higher levels of details at next time instances and which at lower ones. In this way, dynamic change history maps are created, indicating spatial probabilities of regions needed further 3D modelling at forthcoming instances. Using these maps, predictive assessment can be made, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 5D Digital Cultural Heritage Model (5D-DCHM) is implemented using open interoperable standards based on the CityGML framework, which also allows the description of additional semantic metadata information. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 5D-DCHM geometry and the respective semantic information. The open source 3DCityDB incorporating a PostgreSQL geo-database is used to manage and manipulate 3D data and their semantics.
Conference paper (PDF, 1895 KB)


Citation: Doulamis, A., Doulamis, N., Ioannidis, C., Chrysouli, C., Grammalidis, N., Dimitropoulos, K., Potsiou, C., Stathopoulou, E.-K., and Ioannides, M.: 5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W3, 61-68, doi:10.5194/isprsannals-II-5-W3-61-2015, 2015.

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