Investigating Material Decay of Historic Buildings using Visual Analytics with Multi- Temporal Infrared Thermographic Data Maria Danese* , ** Urška Demšar***, Nicola Masini*, Martin Charlton*** * National Counsil of Research Archaeological and Monumental Heritage Institute, **Università degli Studi della Basilicata, Dipartimento di Architettura, Pianificazione ed Infrastrutture di Trasporto ***National Center for Geocomputation
19
Embed
Investigating material decay of historical buildings using visual analytics with multi-temporal infrared thermographic data Urska Demsar, Martin Charlton – National Centre for Geocomputation,
Investigating material decay of historical buildings using visual analytics with multi-temporal infrared thermographic data Urska Demsar, Martin Charlton – National Centre for Geocomputation, National University of Ireland , Maynooth ( Ireland ) Nicola Masini, Maria Danese – Archaeological and monumental heritage institute, National Research Council, Potenza ( Italy ) Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Investigating Material Decay of Historic Buildings using Visual Analytics with Multi-
Temporal Infrared Thermographic Data
Maria Danese*,** Urška Demšar***, Nicola Masini*, Martin Charlton***
* National Counsil of Research Archaeological and Monumental Heritage Institute,
**Università degli Studi della Basilicata,Dipartimento di Architettura, Pianificazione ed
Infrastrutture di Trasporto***National Center for Geocomputation
Infrared Thermography: introduction
A remote sensing technique
Many application fields
IRT for Cultural Heritage (decay research)
Infrared Thermography: introduction
J = σ·T4 Black body model
- J = exitance, radiation emitted per unit of surface (W/m2)- σ is Stefan-Boltzmann’s constant (5.67 × 10-8 W/m2K4) - T is the absolute temperature (°K)
J = ε·σ·T4 Gray body model
- ε = emissivity
The problem: material characterization and decay research
Large number of parameters involved in the process of the heat transfer
specific heat)• Geometric properties (porosity, volumetric mass)
Big size and dimensionality of multi-temporal IR dataset (ten thousand of pixel per thermogram…)
The problem: material characterization and decay research
Spatial continuity of materials: spatial clusters
Thermal inertia of materials: temporal clusters
Methods: Visual Analytics of multi-temporal infrared thermographic
imagery Definition: visual spatial data analysis as a part of exploratory spatial data analysis employs visual exploration of large data sets in order to identify spatio-temporal and other patterns that subsequently serve as basis for hypothesis generation and analytical reasoning about the data and the phenomenon that generated these data.
Environment built using Geovista Studio*:- Self-Organising Map (SOM)- Temporal Parallel coordinates- Parallel coordinates plot linked to SOM- A map linked to the SOM
*Gahegan et al. 2002
Methods: the Self-Organizing Map (SOM)
It maps a multidimensional space in a bidimensional one
The output space
• is a regular grid or hexagonal lattice
• Has two types of cells: node cell, distance cells GeoVISTA Studio SOM (Guo et al. 2005)
Methods: the Parallel Coordinates Plot (PCP)
Each polygonal line is the representation of a data element
Each axe represents a dimension of the problem
Methods: the PCP linked to SOM
Each polygonal line is the representation of a node cells of the SOM
Each axe represents a dimension of the problem
Case study: the façade of the Cathedral in Matera, Italy
1. calcarenite surface with a few shallow alveoli (ashlars 1, 2, 3, 4, 5 and 9); 2. light alveolisation (isolated and slightly deeper alveoli) and diffuse erosion of the surface (ashlars 10 and 12); 3. significant alveolisation (alveoli deeper than those of the pattern 2) that start to be connected (ashlars 7, 13 and14);4. strong alveolisation and irregular surface (ashlars 6, 8 and part of ashlar 11); 5. dark coloured crust probably attributable to a past protective treatment (ashlar 11);6. the behaviour of the mortar between ashlars;7. other phenomena that are not recognisable in the photo taken in visible light, such as for example the presence of humidity in the wall.
Acquisition of IR thermal images and pre-processing of the data
Thermal camera used characteristics• AVIO TVS 600 microbolometric• long wave spectrum (8 ÷14 μm,)• lens of 35 mm • target range of 3.30m • spatial resolution is 1.4 mrad
Thermograms : spatial resolution is 4.62mm
Results of first experiment
Results of first experiment
Results of first experiment
Results of first experiment
Results of first experiment
Results of first experiment
Results of first experiment
to use this approach to study•More materials•Different kind of decay
to map identified patterns
to give a practical help for restoration of the building (economic advantages)
to iteratively re-evaluate and control the restoration results at every step during the restoration process.