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

Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems

Published 2025-10-22

  • Priya R
    • ,
  • Rajiv R Bhandari
    • ,
  • Vilas Namdeo Nitnaware
    • ,
  • Ramesh P N
    • ,
  • Sivakumar R
    • ,
  • Dhanapal R

Priya R
Department of Computer Science and Applications (MCA, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India.

Rajiv R Bhandari
Department of Computer Engineering, SNJB's Late Sau K B Jain College of Engineering, Chandwad - 423101, Nashik, Maharashtra, India.

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

Ramesh P N
Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore, India.

Sivakumar R
Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore 560029, India.

Dhanapal R
Faculty of Engineering, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India.

Abstract

Increasing climate variability and the rapid degradation of natural ecosystems have necessitated the development of intelligent systems that can track and assess environmental changes in real-time. By combining multi-modal remote sensing data with advanced machine learning and visual analytics techniques, this paper introduces a novel framework for Remote-Sensed Intelligent Visual Analytics (RS-IVA), which aims to improve environmental monitoring systems. To offer a comprehensive, scalable, and adaptable monitoring system, the proposed framework utilizes ground sensor inputs, UAV-based aerial photography, and high-resolution satellite imaging. To identify anomalies such as deforestation, urbanization, water pollution, and changes in air quality, a hybrid deep learning-based algorithm is employed. Explainable AI (XAI) elements make sure that the decision-making process is transparent and accessible. To assist stakeholders, investigate spatiotemporal patterns, forecast environmental hazards, and enhance evidence-based policy decisions, an interactive visual analytics dashboard is being developed. Experiments using benchmark datasets demonstrate that the system is highly accurate in identifying significant environmental changes and exhibits greater adaptability across a wide range of climatic and geographic regions. Intelligent analytics and remote sensing technologies collaborate to improve situational awareness and provide early warnings for sustainable resource planning and disaster management. This research advances the development of next-generation innovative environmental monitoring systems by integrating human-in-the-loop visualization, AI-driven analytics, and remote sensing for informed ecological governance.

Keywords

Remote Sensing, Intelligent Visual Analytics, Environmental Monitoring Systems, Deep Learning, XAI, Spatiotemporal Analysis, UAV and Satellite Imagery

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