Nowadays, cultural and historical built heritage can be more effectively preserved, valorised and documented using advanced geospatial technologies. In such a context, there is a major issue concerning the automation of the process and the extraction of useful information from a huge amount of spatial information acquired by means of advanced survey techniques (i.e., highly detailed LiDAR point clouds). In particular, in the case of historical built heritage (HBH) there are very few effective efforts. Therefore, in this paper, the focus is on establishing the connections between semantic and geometrical information in order to generate a parametric, structured model from point clouds using ontology as an effective approach for the formal conceptualisation of application domains. Hence, in this paper, an ontological schema is proposed to structure HBH representations, starting with international standards, vocabularies, and ontologies (CityGML-Geography Markup Language, International Committee for Documentation conceptual reference model (CIDOC-CRM), Industry Foundation Classes (IFC), Getty Art and Architecture Thesaurus (AAT), as well as reasoning about morphology of historical centres by analysis of real case studies) to represent the built and architecture domain. The validation of such schema is carried out by means of its use to guide the segmentation of a LiDAR point cloud from a castle, which is later used to generate parametric geometries to be used in a historical building information model (HBIM).

The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.

The unprecedented pace of urbanization has been exerting significant strain on cities, raising critical concerns across environmental, economic, social, and technological aspects. In response, the ‘Smart City’ concept has emerged as a novel urban development paradigm, aiming at addressing contemporary urban issues, enhancing cities’ competitiveness and prosperity, and fostering active participation through the strategic utilization of state-of-the-art technologies. However, the smart city term suffers from considerable conceptual ambiguity, thereby provoking intense confusion and misunderstanding among interested parties and leading to the implementation of ineffective initiatives. Moreover, the priorities of sustainability, resilience, and inclusiveness have gained prominence in the urban planning discourse, necessitating a more integrated view that aligns urban targets with performance assessment across various domains. In light of these issues, this study endeavors to clarify the above-mentioned conceptual vagueness by developing a holistic, indicator-oriented smart city ontology. The proposed knowledge representation scheme is intended to serve as a Decision Support Tool that will facilitate policymakers to tackle urban challenges and formulate sound policies. Additionally, it is expected to contribute to the fields of spatial and developmental planning by establishing a standardized framework for assessing and monitoring cities’ performance, while elucidating the complex interrelationships and trade-offs among diverse urban dimensions.