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IET Smart Cities 2020 : IET Smart Cities: Geospatial Sensing, Mining & Analytics for Smart Cities

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Link: https://digital-library.theiet.org/files/IET_SMC_CFP_GSMASC.pdf
 
When N/A
Where N/A
Submission Deadline Mar 15, 2020
Notification Due Sep 29, 2020
Final Version Due Sep 29, 2020
Categories    smart cities   geospatial   urban analytics   machine learning
 

Call For Papers

Geospatial data is central to the challenges and opportunities for smart city research. Geospatial data is derived from a broad array of sensing technologies and devices, including satellites, UAV-mounted cameras, distributed sensor networks for air quality monitoring, and GPS-enabled devices equipped in vehicles and smartphones. This data presents great opportunities for researchers to enable smart city applications and to advance geospatial research.
Smart Cities are innovative users of data and infrastructures, improving cities functionality, efficiency, competitiveness, and sustainability. Smart Cities encourage the involvement of all its citizens in the city’s governance and innovation processes. Hence, geospatial data is at the heart of smart cities. Geospatial data can help to measure, understand, and predict the flows and movements of citizens, and the efficiency of infrastructure. These can be used for more accurate prediction of future demands, better planning, and decision-making. Data quality issues, challenges to do with high volume real-time data streaming, scalability of data analysis and learning techniques introduce interesting challenges.
This special issue invites original research papers on the latest advances in geospatial data and smart cities, such as scalable geospatial data mining techniques or GeoAI for smart cities, with applications to transportation, energy use, health, citizens’ engagement, and city planning.
Topics of interest include, but are not limited to:
• Scalable geostatistics for smart cities • High frequency cities and geospatial data analytics • Nowcasting using geospatial real-time data • Data mining, Machine learning and AI techniques for spatial-temporal analytics of city data • GIS for intelligent city planning and waste management • Applications of GIS for smart cities
• Public mapping for better citizens engagement • Crowdsourcing and mobile data gathering techniques • IoT, LiDAR, and UAV geospatial sensing techniques • Multi-dimensional GIS: mobile GIS • 3D City Modelling and Analysis • GPS, GNSS, remote sensing, image processing • City area surveying and geomatics • Environmental modelling and mapping • Standards and Spatial Data Infrastructure (SDI)

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