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

 

 

Special Issue Editors

 

Dr. Mohammed Wasim Bhatt
Model Institute of Engineering and Technology,
Jammu, Jammu and Kashmir,
India.
Email: wasim.cse@mietjammu.in
Google Scholar, Scopus, ORCID

 

Dr. Evans Asenso
Department of Agricultural Engineering,
University of Ghana, Accra,
Ghana.
Email: easenso@ug.edu.gh
Google Scholar,  Scopus, ORCID

 

 

 

Special Issue Information  

The world is becoming an increasingly digital place, where data conversion into information using intelligent computing systems has become a routine process in many aspects of our lives. Living in a digital age offers abundant opportunities to make decisions by effectively managing the available information at the right time and place. Currently, dense cities present a growing need to ensure sustainable urban environments. As the population of metropolitan areas continues to grow and buildings continue to be constructed, there is increasing pressure on these cities' infrastructure. Using an advanced sensing platform, smart sensors and data analytics are key solutions for sustaining such a city. Real-time Environmental Monitoring Systems are integrated into many aspects of planning, managing, and operating cities for infrastructure and human health benefits, enabling efficient monitoring of sustainable living environments. This system also analyzes emissions from sources that affect air quality and identifies the specific compounds emitted. It uses data collected in real time, directly or indirectly, from any physical property or phenomenon that can be monitored over time.

Remote-sensing intelligent visual analytics is a novel approach that was developed to significantly improve the accuracy and reliability of widespread video surveillance systems for real-time security monitoring applications. It combines environmental and earth understanding with advanced data mining, pattern recognition, and predictive capabilities to produce actionable insights for decision makers when time is critical. Remote-sensed intelligent visual analytics for real-time environmental monitoring systems provide the entire environment with intelligence, a humanized operation strategy, and quality service. This system adopts the inferential reasoning method to build a context model based on online image processing of video captured by the camera, and metadata information from the environment, such as temperature and humidity, is collected through sensor networks to enrich the inference result. It effectively integrates an intelligent visual analytics system with data-collection systems. When reviewing real-time videos, it is often important to obtain information about the context of an environment, such as what objects exist in the monitored area or where and how far a target can be situated specifically. However, the real-time monitoring system of an environment is a complex task because of the large amount of information and occurrences that must be processed in a limited time and their dynamics. Hence, finding more research based on remote-sensed intelligent visual analytics is important, which can support decision-makers by showing urgent events and essential aspects in consideration of transient environments. This special issue investigates remotely sensed intelligent visual analytics for real-time environmental monitoring.

 

Topics of interest for the special issue include, but are not limited to, the following:

• Designing remote-sensed intelligent visual analytics algorithms for environmental monitoring systems
• Effective ways of image and video recognition in environmental and earth monitoring systems using remote sensed intelligent visual analytics
• Big data-based remote sensed intelligent visual analytics for environmental and earth monitoring
• Data-driven computational intelligence visual analytics for environmental monitoring
• Smart environmental and earth monitoring systems using remote sensed intelligent visual analytics
• Visual analytics for spatial time series data visualization for environmental management
• Earth model observation using remote-sensed intelligent visual analytics
• IoT sensing data for remote-sensed smart visual analytics in environmental management
• Sustainable environmental management using remote-sensed smart visual analytics
• Remote-sensed computing for environmental observation
• Application of machine learning and artificial intelligence in real-time environmental monitoring systems.

Deadline for manuscript submissions: 30 November 2025.

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