Investigating Material Decay of
Historic Buildings using Visual
Analytics with Multi-Temporal
 Infrared Thermographic Data...
Infrared Thermography: introduction


        A remote sensing technique



          Many application fields



 IRT f...
Infrared Thermography: introduction


                     J = σ·T4         Black body model




-   J = exitance, radiati...
The problem: material characterization
                 and decay research

 Large number of parameters involved in the
 ...
The problem: material characterization
              and decay research

 Spatial continuity of materials: spatial cluste...
Methods: Visual Analytics of multi-temporal
    infrared thermographic imagery

Definition: visual spatial data analysis a...
Methods: the Self-Organizing Map (SOM)


 It maps a
  multidimensional space
  in a bidimensional one

   The output spa...
Methods: the Parallel Coordinates Plot
                 (PCP)




 Each polygonal line is the representation of a data
  ...
Methods: the PCP linked to SOM




 Each polygonal line is the representation of a node cells
  of the SOM

      Each a...
Case study: the façade of the Cathedral in
                    Matera, Italy




1. calcarenite surface with a few shallow...
Acquisition of IR thermal images and pre-
          processing of the data
 Thermal camera used characteristics
   •   AV...
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
Future goals


 to use this approach to study
   •More materials
   •Different kind of decay

 to map identified pattern...
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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

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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)

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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

  1. 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. 2. Infrared Thermography: introduction  A remote sensing technique Many application fields IRT for Cultural Heritage (decay research)
  3. 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. 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. 5. The problem: material characterization and decay research  Spatial continuity of materials: spatial clusters  Thermal inertia of materials: temporal clusters
  6. 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. 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. 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. 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. 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. 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. 12. Results of first experiment
  13. 13. Results of first experiment
  14. 14. Results of first experiment
  15. 15. Results of first experiment
  16. 16. Results of first experiment
  17. 17. Results of first experiment
  18. 18. Results of first experiment
  19. 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.

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