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
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