City Information Modeling

By creating a dynamic 3D model of a city's physical and functional characteristics, CIM integrates data from geographic information systems (GIS), building information modelling (BIM) and other sources. This unified digital representation provides an in-depth, real-time view of urban infrastructure.
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Technology Readiness Level (TRL)

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Prototype Testing

Prototype is fully functional and ready for testing in industrially relevant environment.

City Information Modeling

This solution addresses the complex challenges of modern city management. As urban areas continue to expand and evolve, cities face issues such as inefficient resource allocation, inadequate infrastructure maintenance, and poor urban design. City Information Modelling (CIM) offers a comprehensive solution by integrating various data sources into a unified digital representation of the city's physical and functional characteristics.

CIM is a sophisticated digital tool that creates a dynamic 3D model of an urban environment, incorporating data from geographic information systems (GIS), building information modelling (BIM), and other relevant sources. This model provides an in-depth, real-time view of the city's infrastructure, including buildings, roads, utilities, and public spaces. By synthesising this information, CIM enables urban planners, architects, and city officials to make more informed decisions regarding development, maintenance, and sustainability.

The technology operates by collecting and analysing vast amounts of data from sensors, satellites, and other data-gathering technologies. This data is then processed to create an interactive, visual representation of the city. Users can explore different aspects of the urban environment, run simulations, and predict outcomes of various planning scenarios. For instance, CIM can simulate the impact of new construction projects on traffic flow, environmental sustainability, and public safety, providing valuable insights before any physical changes are made. CIM also supports efficient disaster response, optimised resource use, and sustainable urban development, thereby enhancing the overall quality of life in cities.

As urban populations grow, the need for efficient, sustainable, and resilient city planning becomes critical. CIM enables cities to optimise their resources, enhance public services, and improve the quality of life for their inhabitants. By providing a holistic view of the urban environment, CIM supports data-driven decision-making, ensuring that development is both strategic and responsive to the needs of the community.

Future Perspectives

As the 3D model of the entire urban area is updated continuously with real-time pictures and videos taken from CCTV cameras, this could raise privacy concerns over the lives of the citizens captured by these systems. In this sense, ethical and moral discussions would need to take place for this technology to be successfully deployed. Another solution for this issue is to employ real-time machine vision to automatically recognize and blur all human faces or even full bodies to help avoid possible voyeur-like tracking or spying on individuals.

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Sources
Geospatial Leader Bridges the divide between IoT systems and Geographic Information Systems (GIS), and is designed specifically for all of your location-aware connected assets, mobile and stationary.
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'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload,
CityScopes are interactive, digital city models that analyze urban relationships and simulate development scenarios.They typically consist of model tables and "data blocks" on which information is projected. In this way, complex city data can be illustrated simply and transparently for concrete tasks and experimentally carried out as "what if" scenarios. CityScopes are particularly suitable for group discussions and participation workshops in which both professionals as well as laymen can participate. Multifunctional relationships can be displayed and changed quickly, with CityScopes providing fast visual feedback on potential impacts.
Smart sensors to monitor water use and flow through underground infrastructure are being developed by a project team originating from the Singapore-MIT Alliance for Research and Technology. The group is helping the government of Singapore set up a sensor active network called WaterWiSe.
How much urban area can we monitor by putting sensors on taxis?
Array of Things received a $3.1 million grant from the National Science Foundation! Find out more here, check out our new FAQ, and read coverage from Crain's Chicago Business, Chicago Magazine, Government Technology, and WBEZ.
3,200 intelligent sensor nodes will help optimize traffic and parking, plus enhance public safety, environmental awareness and overall livability for San Diego residents Current’s open platform unlocks potential of software development community by providing access to real-time sensor data for future intelligent city applications San Diego to additionally upgrade 25 percent of outdoor lighting with latest LED technology; 14,000 new LED light fixtures expected to save $2.4 million in annual energy costs
Sustainable use and management of energy resources is a challenging task for growing urban population. Especially, an urban environment in temperate continental climate consumes high energy resources for their space heating and domestic hot water demands. Assessment of thermal energy requirements for future energy demands is fundamental to sustainable urban environment. Thermal energy demand can be simulated using physical and empirical laws from building physics domain. Physical laws compute thermal energy consumption based on heterogeneous datasets from various data sources. These datasets may include information from cadastre, building registers, inhabitant census, 3D building models, ground surveys and meteorological databases. Furthermore, depending upon availability, accessibility and level of detail, specific simulation methods are usually employed for evaluation of energy consumption at different spatial scales. This paper attempts to identify input data parameters which could facilitate validation and calibration of thermal energy demand based on input data sensitivity using simulation methods.
Delve empowers urban development teams to design better neighborhoods, faster, with less risk.
Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.
this paper introduces a simulation environment developed for analyzing crowd-sensing based applications in the Smart City application domain. As a case study, an urban parking application scenario is investigated and presented. In this scenario, smart citizens collect and share parking related events, such as leaving or occupying a free parking spot.
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