Search

To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

Urbanization is a continuous evolution process that is currently studied by a number of researchers. Multi-source and multidimensional city information models are often used to understand the ever-changing urban landscape. These models may encounter issues with interoperability and data-loss if conversion is required for integration. Today we can base ourselves on conceptual models which can help deal with data losses during conversion and may also help preserve data interoperability. This kind of model-driven approach can be useful as common representation. Recently, a movement towards graph and semantic based data representations has also grown in popularity to respond to these issues. As a first step, we consider CityGML, a common standard that can be used to represent 3D urban information. We propose a strategy for converting the semantics of CityGML conceptual model into ontologies and later to semantic web formats to facilitate integration. In addition, we propose a method for converting and storing CityGML instances into RDF individuals that respect the generated ontology. This proposed approach overcomes the loss of semantic information resulting from the direct translation of different types of data into graphs such as RDF, L'urbanisation est un processus d'évolution continue qui est actuellement étudié par nombre de chercheurs. Les modèles d'information de la ville, multi-sources et multidimensionnels, sont souvent utilisés pour comprendre le paysage urbain, qui est en constante évolution. Ces modèles peuvent cependant poser des problèmes d'interopérabilité et de perte de données lors de conversions, souvent nécessaires pour permettre leur intégration. Aujourd'hui, nous pouvons nous baser sur des modèles conceptuels qui peuvent aider à traiter les pertes de données lors de conversions, et peuvent également contribuer à préserver l'interopérabilité des données. Ce type d'approche modèle-centrée permet de définir une représentation commune. Récemment, un mouvement vers la représentation de données basée sur les graphes et la sémantique a gagné en popularité pour répondre aux problèmes d'interopérabilité. Dans un premier temps, pour illustrer notre approche, nous considérons CityGML, un standard de représentation des informations urbaines en 3D. Nous proposons une stratégie pour convertir la sémantique du modèle conceptuel de CityGML vers des ontologies et, ensuite, en formats web sémantiques pour faciliter l'intégration. Nous proposons également une méthode pour convertir et stocker les instances CityGML en individus RDF qui respectent l'ontologie générée. L'approche proposée permet de pallier le déficit d'information sémantique résultant de la traduction directe de différents types de données en graphe, tels que RDF.

Granularity deals with organizing in greater or lesser detail data, information, and knowledge that resides at a granular level. This organization is carried out according to certain criteria, which thereby provide a context view or dimension also called granular perspective. Topological relations express spatial associations among geospatial features (points, polylines, and polygons); they represent a horizontal spatial analysis. The two domains allow scientists to conceive different perspectives of the world. In this article, we aim to combine the two representations through Description Logics (DL) rules to relate granular (vertical representation) and geospatial topological (horizontal representation) relations. The following consequences are thus noted: (1) geospatial features become granules, (2) geospatial features are grouped into different levels of granularity and different granules, and finally, (3) granular construction and decomposition operations are integrated into the spatial domain.

The Coordinated Online Information Network (COIN) is a spatial data infrastructure (SDI) which provides an online network of resources to share, use and integrate information of geographic locations in North Canada. COIN incorporates semantic web technology that integrates, publishes and visualizes time series water data allowing users to access a multitude of datasets in order to compare the data and draw conclusions. COIN utilizes a number of standards from OGC (Open Geospatial Consortium) and W3C (Resource Description Framework, RDF, Web Ontology Language OWL, SPARQL query language for RDF) and GeoSPARQL for geospatial query). COIN benefits from generic ontologies transforming data into semantics, enriching data sets and making the data available and interoperable via WFS and WMS standards. These principles facilitate publication and exchange of data across the web, increasing transparency and interpretability. Through modernized data submission and retrieval we hope to break down the silos of data, allowing users to visualize time series water quality and hydrometric data from multiple sources to increase knowledge in relationship to impacts on Yukon water.