Special Issues

Applications of Artificial Intelligence in Geomatics for Environmental Monitoring

Submission Deadline: 06 October 2024 View: 18 Submit to Special Issue

Guest Editors

Assoc. Prof. Dr. Mustafa Ustuner, Department of Geomatic Engineering, Artvin Coruh University
Mustafa Ustuner earned his PhD and MSc degrees in Geomatic Engineering from Yildiz Technical University, Türkiye. He was a short-term visiting researcher in Geo-Spatial Analytics Lab at the University of South Florida in the United States and a visiting researcher in the Department of Earth Observation at the Friedrich-Schiller-University of Jena in Germany, during his graduate studies. Currently, he is working as an assistant professor for the Department of Geomatic Engineering in Artvin Coruh University, Türkiye. As an editorial task, he has been serving as an associate editor for the European Journal of Remote Sensing (indexed in WoS) and Arabian Journal of Geosciences. His main research interests include Synthetic Aperture Radar (SAR) Remote Sensing, Machine Learning and particularly ensemble learning algorithms. Recently, he is working on dimensionality reduction and classification of hyperspectral images.

Assoc. Prof. Dr. Mahmut Oguz Selbesoglu, Istanbul Technical University
Dr. Selbesoglu received his PhD and MSc degrees in Geomatic Engineering from Yildiz Technical University and is now currently working as an associate professor for the department of Geomatic Engineering in Istanbul Technical University, Turkey. He was a visiting scholar at Vienna University of Technology during his PhD. His main research interests include Atmospheric Remote Sensing, GNSS Data Analysis for sea level monitoring.

Summary

In the last decade, Artificial intelligence (AI) is having a transformative impact and paradigm shift on Geomatics Science and Engineering, which encompasses the collection, analysis, and interpretation of geospatial data. AI algorithms can be used to analyse large and complex geospatial datasets to extract proper/meaningful information and patterns that would be difficult or almost impossible to detect manually. This information can then be used to make better decisions about land use/land cover dynamics, urban/rural interactions, disaster preparedness, and environmental management and assessment.

 

In this Special Issue, we would like to invite you to submit original research related to the applications of AI in Geomatics for the environmental monitoring. Comprehensive reviews of this topic are also welcome.

 

The following topics/subtopics, but are not limited to, will be considered for this Special Issue:

 

- Applications of Machine and Deep learning in Geomatics (incl. remote sensing, geodesy, GIS, and surveying)

- Remote Sensing and Geodetic Applications for Vegetation Analysis

- Land Use/Cover Classification using Optical/SAR/UAV data

- Applications of AI for Atmospheric Remote Sensing (incl. sea level monitoring, atmosphere modelling and monitoring, GNSS data processing for climate monitoring)

- Applications of AI for feature extraction, classification, object recognition, change detection and domain adaptation

- Machine/Deep Learning for the classification and regression analysis of Earth Observation data

- The use of spaceborne as well as UAVs/airborne data in Antarctica and in Polar Regions


Keywords

Geomatics, Artificial Intelligence, Machine Learning, Remote Sensing, Deep Learning, Atmospheric Remote Sensing

Published Papers


  • Open Access

    ARTICLE

    Harnessing ML and GIS for Seismic Vulnerability Assessment and Risk Prioritization

    Shalu, Twinkle Acharya, Dhwanilnath Gharekhan, Dipak Samal
    Revue Internationale de Géomatique, Vol.33, pp. 111-134, 2024, DOI:10.32604/rig.2024.051788
    (This article belongs to the Special Issue: Applications of Artificial Intelligence in Geomatics for Environmental Monitoring)
    Abstract Seismic vulnerability modeling plays a crucial role in seismic risk assessment, aiding decision-makers in pinpointing areas and structures most prone to earthquake damage. While machine learning (ML) algorithms and Geographic Information Systems (GIS) have emerged as promising tools for seismic vulnerability modeling, there remains a notable gap in comprehensive geospatial studies focused on India. Previous studies in seismic vulnerability modeling have primarily focused on specific regions or countries, often overlooking the unique challenges and characteristics of India. In this study, we introduce a novel approach to seismic vulnerability modeling, leveraging ML and GIS to address… More >

  • Open Access

    ARTICLE

    Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

    Suman Kunwar, Jannatul Ferdush
    Revue Internationale de Géomatique, Vol.33, pp. 1-13, 2024, DOI:10.32604/rig.2023.047627
    (This article belongs to the Special Issue: Applications of Artificial Intelligence in Geomatics for Environmental Monitoring)
    Abstract As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in Artificial Intelligence (AI), computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with red-green-blue More >

    Graphic Abstract

    Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

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