PriMera Scientific Engineering (ISSN: 2834-2550)

Research Article

Volume 4 Issue 1

Urban Computing: A Holistic Framework Tailored for 3D Geosimulation and Enlightened Prospective Analysis

Igor AGBOSSOU*

December 19, 2023

DOI : 10.56831/PSEN-04-098

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Abstract

Urban Computing (UC) stands as an interdisciplinary field where urban challenges are examined and, where applicable, addressed through cutting-edge computing technologies. The swift pace of urbanization has brought about significant improvements in many aspects of people's lives, but it has also given rise to substantial challenges like traffic congestion, energy consumption, pollution, soil artificialization, and heat islands. In response, Urban Computing seeks to confront these issues by leveraging the data generated in cities, facilitated by urban sensing, data management, data analytics, and service provision. This iterative process aims for unobtrusive and continuous enhancements in the quality of life, city operations, and environmental conditions. This paper introduces a comprehensive framework tailored for Urban Computing, specifically attuned to the requirements of 3D geosimulation and informed prospective analysis. Given the dynamic evolution of urban environments, the demand for sophisticated computational tools has become increasingly imperative. The proposed framework integrates cutting-edge technologies to address the intricacies associated with urban dynamics, providing a foundational basis for well-informed decision-making. Encompassing components for data acquisition, processing, modeling, simulation, and analysis, the framework underscores the synergy among these elements, promoting a holistic understanding of urban phenomena.

Keywords: Urban Sensing; Urban Data Analytics; Explainability, Smart Cities; Sustainable Development Goals; Machine Learning

References

  1. Hui Lin., et al. “Virtual Geographic Environments (VGEs): A New Generation of Geographic Analysis Tool”. Earth-Science Reviews 126 (2013): 74-84.
  2. Benenson I and Torrens P. “Geosimulation: Automata-based modelling of urban phenomena”. Wiley: Chichester (2002).
  3. Agbossou I. “Urban Augmented Reality for 3D Geosimulation and Prospective Analysis”. In: Pierre Boulanger. Applications of Augmented Reality - Current State of the Art. [Working Title] IntechOpen (2023).
  4. Bazzanella Liliana., et al. Future Cities and Regions. “Simulation, Scenario and Visioning, Governance and Scales”. New York, Heidelberg: Springer (2011).
  5. Batty M. “Cities as Complex Systems: Scaling, Interaction, Networks, Dynamics and Urban Morphologies”. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY (2009).
  6. Portugali J. (Eds.). Handbook on Cities and Complexity. Cheltenham, UK: Edward Elgar Publishing (2021).
  7. Giuliani G., et al. “Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation”. International Journal of Applied Earth Observation and Geoinformation 88 (2020): 102068.
  8. Yigitcanlar T, Agdas D and Degirmenci K. “Artificial intelligence in local governments: Perceptions of city managers on prospects, constraints and choices”. AI & Society (2022a).
  9. Zhou M., et al. “SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion”. Environmental Science and Pollution Research 29.48 (2022): 72797-72818.
  10. Hajjaji Y., et al. “Big data and IoT-based applications in smart environments: A systematic review”. Computer Science Review 39 (2021): 100318.
  11. Herath H and Mittal M. “Adoption of artificial intelligence in smart cities: A comprehensive review”. International Journal of Information Management Data Insights 2.1 (2022): 100076.
  12. Sanchez T., et al. “The prospects of artificial intelligence in urban planning”. International Journal of Urban Sciences (2022).
  13. Alix-Garcia J., et al. “Drivers of forest cover change in Eastern Europe and European Russia, 1985-2012”. Land Use Policy 59 (2016): 284-297.
  14. Manogaran G and Lopez D. “A survey of big data architectures and machine learning algorithms in healthcare”. International Journal of Biomedical Engineering and Technology 25 (2017): 182.
  15. Berawi M. “The role of industry 4.0 in achieving sustainable development goals”. International Journal of Technology 10.4 (2019): 644-647.
  16. Imdat A, Prithwish B and Pratap T. “Artificial Intelligence in Urban Planning and Design”. Technologies, Implementation, and Impacts. Elsevier (2022).
  17. Grimaldi D and Carrasco-Farré C. “Implementing Data-Driven Strategies in Smart Cities A Roadmap for Urban Transformation”. Elsevier Science (2021).
  18. Popelka S, Narvaez Zertuche L and Beroche H. “Urban AI Guide”. Urban AI (2023).
  19. Lăzăroiu G and Harrison A. “Internet of Things Sensing Infrastructures and Data-driven Planning Technologies in Smart Sustainable City Governance and Management”. Geopolitics, History, and International Relations 13.2 (2021): 23-36.
  20. Hai Lan, Puyang Zheng and Zheng Li. “Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities”. Technological Forecasting and Social Change 165 (2021): 120569.
  21. Yigitcanlar T., et al. “Artificial intelligence technologies and related urban planning and development concepts: How are they perceived and utilized in Australia?”. Journal of Open Innovation: Technology, Market, and Complexity 6.4 (2020): 187.
  22. Tekouabou S., et al. “Artificial intelligence based methods for smart and sustainable urban planning: A systematic survey”. Archives of Computational Methods in Engineering (2022).
  23. Mikulsen M and Diduck A. “Towards an integrated approach to disaster management and food safety governance”. International Journal of Disaster Risk Reduction 15 (2016): 116-124.
  24. Jha AK., et al. “A review of AI for urban planning: Towards building sustainable smart cities”. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE (2021): 937-944.
  25. Li B., et al. “POI detection of high-rise buildings using remote sensing images: A semantic segmentation method based on multitask attention Res-U-Net”. IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-16.
  26. King BA, Hammond T and Harrington J. “Disruptive technology: Economic consequences of artificial intelligence and the robotics revolution”. Journal of Strategic Innovation and Sustainability 12.2 (2017): 53-67.
  27. Tomitsch M and Haeusler MH. “Infostructures: Towards a Complementary Approach for Solving Urban Challenges through Digital Technologies”. Journal of Urban Technology 22.3 (2015).
  28. City of Los Angeles SmartLA 2028. Technology for a better Los Angeles (2020).
  29. Breeze Technologies.
  30. CurbIQ platform.