Scholarlink

This initiative focuses on creating a cost-effective, real-time smart city monitoring system that reduces reliance on expensive IoT hardware. It tackles key urban challenges like traffic congestion, air pollution, and unpredictable weather by combining computer vision and open data sources. Using TensorFlow, the system analyzes live traffic video feeds, while environmental data such as air quality and weather are fetched from trusted APIs. All this information is displayed through an interactive web dashboard, giving city officials and planners an easy way to monitor conditions and make informed decisions. The platform’s scalable, user-friendly design promotes smarter, more sustainable urban management.

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Real-Time Data Visualization Platform for Smart Cities to Monitor Sustainability Metrics

  • 会议论文

  • 发表日期：

    2026年

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  • B. S. Hemashree,
  • Snehal Ann Alvares,
  • R. Hemanth,
  • Mohamed Adnan,
  • Nasreen Fathima

摘要

This initiative focuses on creating a cost-effective, real-time smart city monitoring system that reduces reliance on expensive IoT hardware. It tackles key urban challenges like traffic congestion, air pollution, and unpredictable weather by combining computer vision and open data sources. Using TensorFlow, the system analyzes live traffic video feeds, while environmental data such as air quality and weather are fetched from trusted APIs. All this information is displayed through an interactive web dashboard, giving city officials and planners an easy way to monitor conditions and make informed decisions. The platform’s scalable, user-friendly design promotes smarter, more sustainable urban management.