2025
Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems

Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems

Real-Time Environmental Monitoring Systems: Integrating Remote Sensing and Intelligent Visual Analytics for Sustainable Urban Development

Published 2025-12-31

  • Kavipriya K
    • ,
  • Vilas Namdeo Nitnaware
    • ,
  • Balaji V. R
    • ,
  • Arul Kumar V P
    • ,
  • Krishnaraj Natarajan
    • ,
  • Hema Kumar M

Kavipriya K
Department of computer science and Applications, Christ Academy Institute for Advanced Studies, Bangalore

Vilas Namdeo Nitnaware
MAEER’S MIT Thane, Near Green Valley Studio, Mira Road, Kashi gaon, Mumbai, Maharashtra - 401107

Balaji V. R
Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore

Arul Kumar V P
Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore.

Krishnaraj Natarajan
Department of Database Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore – 632014

Hema Kumar M
Department of ECE, Sona college of Technology, Salem.

Abstract

The populations in urban centres continue to rise, and cities incur mounting environmental challenges that include worsening air quality, untimely disposal of waste, and uncontrollable changes in the climate. Real-time monitoring of changes in the environment, analysis, and predictions using high-level technologies are necessary to achieve sustainable urban development and resilience. Conventional methods of environmental monitoring do not generally have real-time flexibility, spatial accuracy, and intelligent data analysis, which prevents policymakers from responding in time to impending environmental hazards. A multi-purpose and scalable architecture are needed to combine different sources of data and provide dynamically generated actionable insights. This paper presents the Environmental Cognitive Observation and Intelligent Geospatial Hybrid Tracking (ECO-INSIGHT) model. The approach is a hybrid of unmanned Aerial Vehicles (UAV)-assisted remote sensing, IoT-sensed environmental sensors, and a hybrid deep reinforcement learning model to recognize patterns in real-time and predict events. ECO-INSIGHT deeply integrates a decision fusion layer, which is adaptive, to combine data related to satellites, drones, and ground sensors and provide ongoing environmental intelligence with contextual visualization using AI-powered dashboards. Empirical analysis shows that ECO-INSIGHT increases monitoring accuracy by 94 percent, data redundancy by 38 percent, and predictive response efficiency in a variety of indicators of the ecological condition of cities. ECO-INSIGHT allows proactive environmental management, evidence-based city planning, and sustainable city ecosystems by means of intelligent visual analytics.

Keywords

Remote sensing, IoT, deep reinforcement learning, geospatial analytics, sustainable cities, environmental monitoring

PDF

References

  1. S. Jadhav, M. Durairaj, R. Reenadevi, R. Subbulakshmi, V. Gupta, and J. V. N. Ramesh, “Spatiotemporal data fusion and deep learning for remote sensing-based sustainable urban planning,” International Journal of System Assurance Engineering and Management, pp. 1–9, 2024. Available: https://doi.org/10.1007/s13198-024-02167-1
  2. F. Yao and Y. Wang, “Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data,” Sustainable Cities and Society, vol. 63, p. 102448, 2020. Available: https://doi.org/10.1016/j.scs.2020.102448
  3. D. R. Ramani, B. B. Sujitha, and S. Tangade, “Smart environmental monitoring systems: IoT and sensor-based advancements,” Environmental Monitoring Using Artificial Intelligence, pp. 45–60, 2025. Available: https://doi.org/10.1002/9781394221387.ch4
  4. N. N. Anjum, S. Sumathi, R. D. Dhayalan, R. P. Rampavithran, A. J. Anand, and M. Sathiyanarayanan, “Smart sensors for smart city environmental monitoring systems,” in Digital Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City, pp. 17–33, Elsevier, 2025. Available: https://doi.org/10.1016/B978-0-443-22864-8.00006-9
  5. H. Gu and Y. Wei, “Environmental monitoring and landscape design of green city based on remote sensing image and improved neural network,” Environmental Technology & Innovation, vol. 23, p. 101718, 2021. Available: https://doi.org/10.1016/j.eti.2021.101718
  6. M. Andronie, G. L?z?roiu, O. L. Karabolevski, R. ?tef?nescu, I. Hurloiu, A. Dijm?rescu, and I. Dijm?rescu, “Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the internet of robotic things,” Electronics, vol. 12, no. 1, p. 22, 2022. Available: https://doi.org/10.3390/electronics12010022
  7. M. Poliak, A. Poliakova, O. D. Crî?mariu, and R. ?. Balica, “Visual analytics and digital twin modeling tools, spatio-temporal fusion and predictive modeling algorithms, and deep learning-based sensing and image recognition technologies in data-driven smart sustainable cities and immersive multisensory virtual spaces,” Geopolitics, History, and International Relations, vol. 15, no. 1, pp. 91–105, 2023. Available: https://doi.org/10.22381/GHIR15120236
  8. W. Liu and G. Chen, “Towards optimal image processing-based Internet of Things monitoring approaches for sustainable cities,” International Journal of Advanced Computer Science & Applications, vol. 15, no. 5, 2024. Available: https://doi.org/10.14569/IJACSA.2024.0150514
  9. M. R. Anwar and L. D. Sakti, “Integrating artificial intelligence and environmental science for sustainable urban planning,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 179–191, 2024. Available: https://doi.org/10.34306/itsdi.v5i2.233
  10. M. Al Rawajbeh, S. AlZu'bi, and A. Alkhatib, “IoT-paradigm: Evolution challenges and proposed solutions,” in 2023 IEEE International Smart Cities Conference (ISC2), pp. 1–5, IEEE, Sept. 2023. Available: https://doi.org/10.1109/ISC258198.2023.10274521
  11. A. Salam, “Internet of things for environmental sustainability and climate change,” in Internet of Things for Sustainable Community Development: Wireless Communications, Sensing, and Systems, pp. 33–69, Cham: Springer International Publishing, 2024. Available: https://doi.org/10.1007/978-3-031-51909-3_3
  12. S. L. Ullo and G. R. Sinha, “Advances in smart environment monitoring systems using IoT and sensors,” Sensors, vol. 20, no. 11, p. 3113, 2020. Available: https://doi.org/10.3390/s20113113
  13. T. Sanislav, G. D. Mois, S. Zeadally, and S. C. Folea, “Energy harvesting techniques for Internet of Things (IoT),” IEEE Access, vol. 9, pp. 39530–39549, 2021. Available: https://doi.org/10.1109/ACCESS.2021.3063947
  14. J. Trinder and Q. Liu, “Assessing environmental impacts of urban growth using remote sensing,” Geo-Spatial Information Science, vol. 23, no. 1, pp. 20–39, 2020. Available: https://doi.org/10.1080/10095020.2019.1666694
  15. T. Wellmann, A. Lausch, E. Andersson, S. Knapp, C. Cortinovis, J. Jache, and D. Haase, “Remote sensing in urban planning: Contributions towards ecologically sound policies?” Landscape and Urban Planning, vol. 204, p. 103921, 2020. Available: https://doi.org/10.1016/j.landurbplan.2020.103921
  16. D. Yu and C. Fang, “Urban remote sensing with spatial big data: A review and renewed perspective of urban studies in recent decades,” Remote Sensing, vol. 15, no. 5, p. 1307, 2023. Available: https://doi.org/10.3390/rs15051307
  17. L. Coenen and K. Morgan, “Evolving geographies of innovation: Existing paradigms, critiques and possible alternatives,” Norsk Geografisk Tidsskrift – Norwegian Journal of Geography, vol. 74, no. 1, pp. 13–24, 2020. Available: https://doi.org/10.1080/00291951.2019.1702640
  18. T. Bertaglia, C. M. Costa, S. Lanceros-Méndez, and F. N. Crespilho, “Eco-friendly, sustainable, and safe energy storage: A nature-inspired materials paradigm shift,” Materials Advances, vol. 5, no. 19, pp. 7534–7547, 2024. Available: https://doi.org/10.1039/D4MA00321G
  19. X. Ma, J. Li, Z. Guo, and Z. Wan, “Role of big data and technological advancements in monitoring and development of smart cities,” Heliyon, vol. 10, no. 15, 2024. Available: https://doi.org/10.1016/j.heliyon.2024.e31250
  20. S. C. Izah, “Smart technologies in environmental monitoring: Enhancing real-time data,” in Innovative Approaches in Environmental Health Management: Processes, Technologies, and Strategies for a Sustainable Future, p. 199, 2025. Available: https://doi.org/10.4018/9781668481421.ch011
  21. W. Li, M. Batty, and M. F. Goodchild, “Real-time GIS for smart cities,” International Journal of Geographical Information Science, vol. 34, no. 2, pp. 311–324, 2020. Available: https://doi.org/10.1080/13658816.2019.1673397
  22. Y. Y. F. Panduman, N. Funabiki, E. D. Fajrianti, S. Fang, and S. Sukaridhoto, “A survey of AI techniques in IoT applications with use case investigations in the smart environmental monitoring and analytics in real-time IoT platform,” Information, vol. 15, no. 3, p. 153, 2024. Available: https://doi.org/10.3390/info15030153
  23. R. Sharma and R. Arya, “UAV-based long range environment monitoring system with Industry 5.0 perspectives for smart city infrastructure,” Computers & Industrial Engineering, vol. 168, p. 108066, 2022. Available: https://doi.org/10.1016/j.cie.2022.108066
  24. R. U. Attah, I. Gil-Ozoudeh, B. M. P. Garba, and O. Iwuanyanwu, “Leveraging geographic information systems and data analytics for enhanced public sector decision-making and urban planning,” Magna Scientific Advances Research Review, vol. 12, no. 2, pp. 152–163, 2024. Available: https://doi.org/10.47890/MSARR.2024.12.2.15
  25. D. Adams, A. Novak, T. Kliestik, and A. M. Potcovaru, “Sensor-based big data applications and environmentally sustainable urban development in Internet of Things-enabled smart cities,” Geopolitics, History, and International Relations, vol. 13, no. 1, pp. 108–118, 2021. Available: https://doi.org/10.22381/GHIR13120217
  26. Z. Shao, N. S. Sumari, A. Portnov, F. Ujoh, W. Musakwa, and P. J. Mandela, “Urban sprawl and its impact on sustainable urban development: A combination of remote sensing and social media data,” Geo-Spatial Information Science, vol. 24, no. 2, pp. 241–255, 2021. Available: https://doi.org/10.1080/10095020.2020.1793692
  27. W. He and M. Chen, “Advancing urban life: A systematic review of emerging technologies and artificial intelligence in urban design and planning,” Buildings, vol. 14, no. 3, p. 835, 2024. Available: https://doi.org/10.3390/buildings14030835
  28. M. A. Haile, D. T. Haile, and D. Zerihun, “Real-time sensor data analytics and visualization in cloud-based systems for forest environment monitoring,” International Journal of Advances in Signal and Image Sciences, vol. 9, no. 1, pp. 29–39, 2023. Available: https://doi.org/10.29284/ijasis.9.1.29
  29. M. A. Ramírez-Moreno, S. Keshtkar, D. A. Padilla-Reyes, E. Ramos-López, M. García-Martínez, M. C. Hernández-Luna, M. A. Jiménez-González, and J. D. J. Lozoya-Santos, “Sensors for sustainable smart cities: A review,” Applied Sciences, vol. 11, no. 17, p. 8198, 2021. Available: https://doi.org/10.3390/app11178198
  30. A. K. Wani, F. Rahayu, I. Ben Amor, M. Quadir, M. Murianingrum, P. Parnidi, M. Hidayatullah, and E. Latifah, “Environmental resilience through artificial intelligence: Innovations in monitoring and management,” Environmental Science and Pollution Research, vol. 31, no. 12, pp. 18379–18395, 2024. Available: https://doi.org/10.1007/s11356-023-30693-3
  31. E. Nica, G. H. Popescu, M. Poliak, T. Kliestik, and O. M. Sabie, “Digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks,” Mathematics, vol. 11, no. 9, p. 1981, 2023. Available: https://doi.org/10.3390/math11091981
  32. S. Beckett, “Smart city digital twins, 3D modeling and visualization tools, and spatial cognition algorithms in artificial intelligence-based urban design and planning,” Geopolitics, History, and International Relations, vol. 14, no. 1, pp. 123–138, 2022. Available: https://doi.org/10.22381/GHIR14120227
  33. https://www.kaggle.com/datasets/ziya07/urbaniot-anomaly-multimodal-smart-city-dataset

Downloads

Download data is not yet available.

Similar Articles

1-10 of 87

You may also start an advanced similarity search for this article.