Classification of surface geometry behavior of cultural heritage surfaces based on monitoring change

Efficient monitoring of large-scale cultural heritage monuments is of great interest in understanding alteration mechanisms that lead to substantial decision making for safeguarding them. This work presents a methodological approach for monitoring areas of the wall of King Jan III’ palace Wilanów (Poland), where due to weathering the documentation of the surface changes became indispensable. Data from 3D scanning and registered data, representing different time intervals, were analysed to determine surface geometry changes. The goal was to develop a methodology which could detect each surface point based on grouping it with similar behaviour of local geometry changes. Further analyses, to determine the direction of change and the local geometry, were performed with the goal to extract information on the behaviour of changes and quantify them. By assigning a gradual scale, for the calculated displacement information regarding alterations can be visualized and measured. The methodology was based on calculating initially the general direction of change and then analysing the local geometry changes based on considering neighbouring points obtained from the spherical search kernel of each surface point. The neighbouring points were then compared to weathered dataset point cloud, by calculating the Euclidean distance and data segmentation, based on histograms of local distance analysis, was produced. Each histogram was fitted to the kernel distribution curve and bandwidth parameters, with similar points, were identified and segmented for changes corresponding to different time intervals. Initial evaluation on the case study shows the ability of the proposed methodology to detect even minor surface displacements.

[1]  Livio De Luca,et al.  Analyzing the evolution of deterioration patterns: A first step of an image-based approach for comparing multitemporal data sets , 2015, 2015 Digital Heritage.

[2]  D. Girardeau-Montaut,et al.  CHANGE DETECTION ON POINTS CLOUD DATA ACQUIRED W ITH A GROUND LASER SCANNER , 2005 .

[3]  Robin Letellier,et al.  Recording, Documentation and Information Management for the Conservation of Heritage Places , 2015 .

[4]  Fabio Remondino,et al.  Classification of 3D Digital Heritage , 2019, Remote. Sens..

[5]  Luca Di Angelo,et al.  Geometric segmentation of 3D scanned surfaces , 2015, Comput. Aided Des..

[6]  Eric P. Xing,et al.  Kernel methods for large-scale genomic data analysis , 2015, Briefings Bioinform..

[7]  D. Lague,et al.  Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) , 2013, 1302.1183.

[8]  Fabio Bruno,et al.  3D documentation and monitoring of the experimental cleaning operations in the underwater archaeological site of Baia (Italy) , 2013, 2013 Digital Heritage International Congress (DigitalHeritage).

[9]  Segmentation of Large Unstructured Point Clouds Using Octree-Based Region Growing And Conditional Random Fields , 2017 .

[10]  Markiewicz,et al.  Review of Methods for Documentation, Management, and Sustainability of Cultural Heritage. Case Study: Museum of King Jan III’s Palace at Wilanów , 2019, Sustainability.

[11]  Pierre Grussenmeyer,et al.  AUTOMATIC HERITAGE BUILDING POINT CLOUD SEGMENTATION AND CLASSIFICATION USING GEOMETRICAL RULES , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[12]  Nicola Lercari Monitoring earthen archaeological heritage using multi-temporal terrestrial laser scanning and surface change detection , 2019, Journal of Cultural Heritage.

[13]  D. Abate,et al.  Built-Heritage Multi-temporal Monitoring through Photogrammetry and 2D/3D Change Detection Algorithms , 2018, Studies in Conservation.

[14]  Xuebo Zhang,et al.  Point cloud segmentation based on Euclidean clustering and multi-plane extraction in rugged field , 2021, Measurement Science and Technology.

[15]  Shuowen Hu,et al.  Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications , 2016 .

[16]  R. Sitnik,et al.  Monitoring surface degradation process by 3D structured light scanning , 2019, Optical Metrology.

[17]  Yi Cao,et al.  A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring , 2019, Processes.