Volume XLII-2/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W11, 831-836, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W11-831-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W11, 831-836, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W11-831-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 May 2019

05 May 2019

TLS FOR DETECTING SMALL DAMAGES ON A BUILDING FAÇADE

A. Masiero1 and D. Costantino2,3 A. Masiero and D. Costantino
  • 1Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, Viale dell’Università 16, Legnaro (PD) 35020, Italy
  • 2DICATECh, Polytechnic of Bari, Italy, 70126 Bari, Italy
  • 3Faculty of Engineering, Salento University, 73100 Lecce, Italy

Keywords: Laser Scanning, Damage Detection, Maintainance, Spatial Filtering

Abstract. Weathering, aging, infiltration, solar radiation and several other factors cause the deterioration of buildings and infrastructures and hence the need for periodical maintenance and restoration. The need for maintenance has been traditionally determined based on visual inspections of qualified operators. Since this process is obviously time consuming and quite expensive, especially when the considered building is quite large and high, then a number of recent studies have been recently published proposing remote sensing tools in order to ease the monitoring process. Among the possible spatial data acquisition sensors, terrestrial laser scanning has been considered in several of the existing studies, mostly because of its high reliability, to cope with cracks and defect detection up to the millimeter level of resolution, which is the typical accuracy of the current generation of professional laser scanners. This paper considers the problem of detecting small defects on the façade of a University building. Similar to other previous studies, in this work defect detection is accomplished by considering distances with respect to a planar surface locally fitted on the building façade. Then, statistical filtering and machine learning tools have been implemented in order to cope with damage detection of the brick surfaces at sub-millimeter level.