Digital acquisition: reflections on data quality
- Title
- Digital acquisition: reflections on data quality
- Creator(s)
- Ippolito, Alfonso
- Attenni, Martina
- Bartolomei, Cristiana
- Date
- 2017
- Is Part Of
-
Archeologia e Calcolatori
- Volume
- 28
- Issue
- 2
- Pages
- 507-519
- Language
- eng
- Rights
- CC BY-NC-ND 4.0
- Short Title
- Digital acquisition
- Abstract
- In the past twenty years archaeological survey has changed radically thanks to the progress in the field of technology, in particular concerning 3D massive acquisition methods. A variety of data acquisition modes, based on active and passive sensor systems, is increasingly easier to access and use to document cultural heritage. The scientific debate focuses primarily on two issues: the use of free or proprietary software, and the control over data quality, in terms of metric accuracy, by comparing 3D image-based acquisition methods with consolidated methods (laser scansion and/or topographic survey). Collecting, interpreting and filing a large amount of information helps to define a system we can use to understand our archaeological heritage. The system is based on the scientific process used to achieve a dual objective: first, to document acquisition using a heterogeneous set of data (x, y, z and RGB) and metadata (information processing) and guarantee repeatability; secondly, to ensure data quality during acquisition and processing. Data processing obtained using 3D massive acquisition methods makes it possible to build models characterized by a biunivocal correspondence to the real object, studied from a geometric and spatial point of view. The study focuses on the shift from quantitative data, acquired in a semi-automatic manner, to qualitative data, meticulously controlled as regards to uncertainty. In this framework, all branches of the Science of Representation ensure metric, spatial and formal control of the built models. The study of the 13th century Gates of the city of Bologna have so far led to the development of a scientific process providing important data about metric quality vis-à-vis, the scale of the model.
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