Cultural Heritage (CH) (In the context of this paper, we consider cultural heritage built tangible cultural heritage, such as buildings or monuments.) is an important source of identity for humankind and needs to be conserved for future generations. Climate change (CC) will morph the environmental landscape, thus leading to climate stress imposed on CH. Experts from different domains, including, but not limited to, material scientists, conservators and managers of cultural heritage collaborate to find out how CC affects CH and how potentially harmful impacts can be mitigated. To find and understand correlations and effects of different factors, researchers collect and analyse vast amounts of data. Still, experts often cannot exchange or make efficient use of data since it often is unstructured, incompatible, or its plain existence is simply unknown. This article introduces means to achieve consent about available knowledge, to exploit synergy effects through the combination of available information and to provide a flexible multisource information platform in collaborative cultural heritage management projects. In the context of the European project HERACLES (HERACLES—HEritage Resilience Against CLimate Events on Site. Further information: http://www.heracles-project.eu/), an application-ontology was developed. The ontology facilitates reuse and integration of data through structuring and representing its semantics. The involvement in the HERACLES project guaranteed end-user driven development, practical results and encompassment of all domains represented in the project.

Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space.

This paper proposes a supervised segmentation method for detecting surface changes based on appearance attributes, focusing on cultural heritage metal surfaces. Reflectance Transformation Imaging (RTI) reconstruction coefficients (PTM and HSH) are explored for tracking changes over time on different data sets. Each acquisition is normalised to ensure the method’s robustness, allowing consecutive acquisitions with different RTI acquisition parameters. The proposed method requires expert labelling on groups of pixels representing individual classes. Afterward, the surface appearance is identified over time based on the estimated discriminant model. After segmentation, each detected category is assigned to a single colour to present the results with a user-friendly colourmap visualisation. The method is user-dependent; the labelling of the pixels must be accurately defined based on the research question. The results were evaluated based on human expertise in the conservation-restoration field and are considered ground truth in this work. A case study with visibly segmentable characteristics was used to prove the concept and evaluate the invariance of the proposed method. Comparison with the segmentation of the visible characteristics shows very accurate segmentation for HSH (99%) and lower for PTM (80%), which is influenced by surface rotation. The method was tested on metal surfaces undergoing accelerated corrosion or cleaning treatments. The results were promising for tracking changes based on segmentation. Equally promising is the possibility of qualitative quantifying the degree of change by counting the change of a selected class of pixels. PTM and HSH results are comparable in cases of mat surfaces; however, in high specular surfaces, HSH seems to provide more detailed information and, therefore, can better depict the surface characteristics. Limitations of the application are related to the possibility of identifying surface characteristics that do not exhibit topographic changes or significant reflectance differentiation.