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

Big Data-Based Remote-Sensed Intelligent Visual Analytics for Environmental and Earth Monitoring Systems

Published 2025-12-12

  • Pramoda Patro
    • ,
  • Sowmya M
    • ,
  • Srinivas N
    • ,
  • Subhranginee Das
    • ,
  • Arul Kumar V P
    • ,
  • Dhanapal R

Pramoda Patro
School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana 506371, India

Sowmya M
Department of Computer Science and Applications (MCA), SRM Institute of Science and Technology (FSH), Ramapuram Campus, Chennai

Srinivas N
Department of CSE, Vignana Bharathi Institute of Technology, Aushapur(V), Ghatkesar

Subhranginee Das
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India

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

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

Abstract

The increasing accessibility of sensor-based earth observations, satellite imaging, and uncrewed aerial vehicle (UAV) data has made environmental monitoring a data-intensive field. However, the size, heterogeneity, and real-time nature of these datasets make it difficult to store, process, and interpret them effectively. This paper proposes a Big Data-driven approach to intelligent visual analytics for earth and environmental monitoring systems. With sophisticated machine learning algorithms, scalable data platforms, and remote sensing technologies, the framework enables automated feature extraction, anomaly detection, and spatiotemporal pattern recognition. Terabytes of satellite and sensor imagery can be analyzed more efficiently through the system's combination of Deep Learning-based Visual Interpretation and Distributed Computing Models (DL-VI-DCM), including Hadoop and Spark. Applications such as land cover classification, deforestation tracking, assessing the effects of climate change, disaster management, and water resource monitoring are all supported by the proposed methodology. To improve decision-making for scientists, environmental agencies, and politicians, a visual analytics dashboard is created to provide interactive maps, time-series trends, and predictive insights. Experimental findings from case studies on urban growth and flood-prone areas show that the framework can process high-resolution remote-sensed data in near real time with high accuracy and interpretability. This paper demonstrates the potential of integrating Big Data technology with intelligent visual analytics to provide flexible, scalable, and decision-focused solutions for earth system monitoring and global environmental sustainability. The proposed DL-VI-DCM achieves superior performance with 40–180?min of processing time, 92–95% classification accuracy, 89–94% anomaly precision, 55–110?GB/min scalability, 80–88% spatiotemporal accuracy, and 1.2–2.5?s response time.

Keywords

Intelligent Visual Analytics, Big Data, Remote Sensaing, Machine Learning, Earth Observation, Environmental Monitoring, Spatiotemporal Analysis

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