CityGML is a widely adopted standard by the Open Geospatial Consortium (OGC) for representing and exchanging 3D city models. The representation of semantic and topological properties in CityGML makes it possible to query such 3D city data to perform analysis in various applications, e.g., security management and emergency response, energy consumption and estimation, and occupancy measurement. However, the potential of querying CityGML data has not been fully exploited. The official GML/XML encoding of CityGML is only intended as an exchange format but is not suitable for query answering. The most common way of dealing with CityGML data is to store them in the 3DCityDB system as relational tables and then query them with the standard SQL query language. Nevertheless, for end users, it remains a challenging task to formulate queries over 3DCityDB directly for their ad-hoc analytical tasks, because there is a gap between the conceptual semantics of CityGML and the relational schema adopted in 3DCityDB. In fact, the semantics of CityGML itself can be modeled as a suitable ontology. The technology of Knowledge Graphs (KGs), where an ontology is at the core, is a good solution to bridge such a gap. Moreover, embracing KGs makes it easier to integrate with other spatial data sources, e.g., OpenStreetMap and existing (Geo)KGs (e.g., Wikidata, DBPedia, and GeoNames), and to perform queries combining information from multiple data sources. In this work, we describe a CityGML KG framework to populate the concepts in the CityGML ontology using declarative mappings to 3DCityDB, thus exposing the CityGML data therein as a KG. To demonstrate the feasibility of our approach, we use CityGML data from the city of Munich as test data and integrate OpenStreeMap data in the same area.

If you live in a city, it's more likely than not that data is being gathered all around you, the majority of the time. From weather conditions, to traffic patterns and parking, the public is often unaware as to the scale of surveillance that is being used to keep things running smoothly. Now, Monash University has released a report detailing how we can use this data ethically in order to better plan for inclusive and smart future cities. The report, from the Emerging Technologies Research Lab (ETLab) in collaboration with the City of Melbourne, zeroes in on how real-time data can be gathered and used, to improve the city and empower the population. Using technologies like artificial intelligence (AI), Internet of Things (IoT) and 5G, local governments can track things like transportation, sanitation, and climate, to improve urban efficiency and enhance quality of life.

Pune has deployed over 1000 IoT devices (including 1500 CCTV cameras, which are quasi IoT devices), connected with the integrated command and control centre (ICCC). The data feeds are regularly relayed from the sensors. Going ahead the many use cases will need to be explored. Pune is working with IISc and IIT Kanpur for use case development, for example, the availability of parking spaces in the city can be easily identified from sensor data; traffic movements in the city can be tracked and appropriate actions relating to reducing congestion can also be taken based on data relayed from the sensors. Pune is the only city in the country to have participated in a global hackathon, wherein the API based technology architecture allows to expose the data in a secure manner globally to create applications over it.

We have been working hard to understand the core stack of data services that make our cities work, or not work, depending on where you live. While we have labeled this research "smart cities", we are starting with the basics of open data required for city operations. This is the current data sets available via existing services, which may or may not exist in a machine readable format, via an API, depending on the city you live in. There is a huge amount of city data already available at the municipal level, but here is where we have started as of January. Now that we have these three critical aspects of municipal operations profiled, we are going to work to profile as many cities as we can.

QuerioCity aims at creating an ecosystem for managing and accessing the information of a city, with a particular focus on transforming, integrating and querying heterogenous semistructured data in an open environment. This raises unique challenges in terms of: - Fitness-for-use. The users of the system are not data integration experts and not qualified to use industry data integration tools. Furthermore, they are not able to query data using structured query languages. The domain of the information is very broad and open.