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)

1. 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

2. Infrared Thermography: introduction
 A remote sensing technique
Many application fields
IRT for Cultural Heritage (decay research)

3. 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

4. The problem: material characterization
and decay research
 Large number of parameters involved in the
process of the heat transfer
• Spectral properties (absorption, reflection, transmission)
• Thermal properties (conductivity, diffusiveness, effusiveness,
specific heat)
• Geometric properties (porosity, volumetric mass)
 Big size and dimensionality of multi-temporal
IR dataset (ten thousand of pixel per
thermogram…)

5. The problem: material characterization
and decay research
 Spatial continuity of materials: spatial clusters
 Thermal inertia of materials: temporal clusters

6. 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

7. 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)

8. Methods: the Parallel Coordinates Plot
(PCP)
 Each polygonal line is the representation of a data
element
 Each axe represents a dimension of the problem

9. 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

10. 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.

11. 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

12. Results of first experiment

13. Results of first experiment

14. Results of first experiment

15. Results of first experiment

16. Results of first experiment

17. Results of first experiment

18. Results of first experiment

19. Future goals
 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.