In recent years new techniques have been proposed for the representation and manipulation of spatio-temporal data that can be usefully applied also in the managing of archaeological data and more in general of cultural heritage information. Indeed, space and time are two crucial dimensions for supporting the interpretation process in the archaeological domain and constitute the backbone of any analysis of artefacts or constructions belonging to the cultural heritage of any village, town or country. Among the others (including ontologies, graph databases, RDF, spatio-temporal relations, evolution modes, virtual reality, etc.) two new research directions are nowadays emerging in the context of the geospatial information science: (i) the approach based on the Building Information Model (or BIM) that aims to describe any construction during its entire life cycle and (ii) the machine learning techniques that allow one to extract and recognize pieces of information from huge image datasets. Several attempts have been recently performed in order to develop solutions that, exploiting the above listed innovative ideas, aim to support: the e ective representation of spatio-temporal data collections for enhancing integration, usability and interoperability; the processing of raw data in order to identify artefacts and de ne their allocation in space and time taking into consideration also their uncertainty; the reconstruction of ancient structures (buildings, walls, castle, etc.) and the representation of their temporal evolution also in case of completely destroyed buildings; the integrated access and querying of the collected data, which are represented in di erent models and formats.
Workshop Program Chairs and Organizers Alberto Belussi, University of Verona, Italy Roland Billen, University of Liege, Belgium Pierre Hallot, University of Liege, Belgium Sara Migliorini, University of Verona, Italy Program Committee Roland Billen, Geomatics Unite ULiege, Belgium Pierre Hallot, Cultural Heritage ULiege, Belgium Alberto Belussi, University of Verona, Italy Sara Migliorini, University of Verona, Italy Piergiovanna Grossi, University of Verona, Italy Eliseo Clementini, University of L'Aquila, Italy Mauro Negri, Politecnico di Milano, Italy Gerald Hiebel, University of Innsbruck, Austria Marco Callieri, Visual Computing Lab of ISTI-CNR, Italy Matteo Dellepiane, Visual Computing Lab of ISTI-CNR, Italy Kathleen Stewart, University of Maryland, USA Pierre Grussenmeyer, INSA Strasbourg, France Livio de Luca, UMR CNRS / MCC Map, France Xavier Rodier, Universite de Tours, France Smart Heritage: Challenges in Digitisation and Spatial Information Modelling of Historical Buildings
Kourosh Khoshelham Mixed Reality for Archeology and Cultural Heritage
Paolo Fogliaroni Deep Learning for Archaeological Object Detection in Airborne Laser Scanning Data
Bashir Kazimi and Frank Thiemann and Katharina Malek and Monika Sester and Kourosh Khoshelham 21 Spatio-Temporal Reasoning in CIDOC CRM: An Hybrid Ontology with GeoSPARQL and OWL-Time
Gilles-Antoine Nys and Muriel Van Ruymbeke and Roland Billen Enhancing CIDOC-CRM Models for GeoSPARQL Processing with MapReduce
Sara Migliorini States of knowledge: a basis for a spatio-temporal model of cultural heritage information Pierre Hallot and Roland Billen 7 13 37 51 67