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

Integration of IoT and Remote-Sensed Visual Analytics for Smart Environmental Surveillance

Published 2025-12-31

  • A Madhuri
    • ,
  • Balamuralikrishna Thati
    • ,
  • Suresh Babu Chandolu
    • ,
  • Sindhura S
    • ,
  • Yalanati Ayyappa
    • ,
  • Ruhisulthana Shaik

A Madhuri
Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada 520007, India

Balamuralikrishna Thati
Department of IT, DVR & Dr. HS MIC College of Technology, Kanchikacherla, NTR District, A.P-521180

Suresh Babu Chandolu
Department of CSE-(AI & ML), Dhanekula Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India-521139

Sindhura S
NRI Institute of Technology

Yalanati Ayyappa
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Tamilnadu, India

Ruhisulthana Shaik
Department of ECE, Sir C Ramalinga Reddy Polytechnic College, Eluru, India

Abstract

This study proposes a unified environmental surveillance system, which fuses IoT sensor networks and the visual analytics of multispectral remote-sensing to address the shortcomings of the conventional surveillance solutions. Edge-based LSTM anomaly detection on distributed nodes of the IoT can offer high-frequency local measurements, whereas a hybrid ResNetVision Transformer (ViT) model can improve the analysis of the satellite image. An adaptive Kalman-based temporal-spatial fusion algorithm incorporates heterogeneous streams of data towards better environmental intelligence. The system was highly performing, indicating the accuracy of the IoT sensors in 91.3-98.1% and a hybrid model at 92.4% and the fused levels at 94% and above respectively. The results were impressive on the system level, since the response time to events was improved significantly, the completeness of data improved, and the accuracy of anomaly detectors increased, as well as the network load decreased. On the whole, the suggested structure has high potential to monitor the environment in real-time, being scalable, in the fields of smart agriculture, air-quality monitoring, water-resource control, climate-risk identification, and smart urban governance.

Keywords

IoT-Based Environmental Monitoring, Remote-Sensed Visual Analytics, Temporal–Spatial Data Fusion, Deep Learning for Environmental Surveillance

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