Deep Learning for Land Use and Land Cover Change Monitoring with Optical and SAR Images
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 348
Special Issue Editors
Interests: synthetic aperture radar (SAR); multi-sensor image processing and fusion; automated geometric correction of multi-sensor images
Special Issues, Collections and Topics in MDPI journals
Interests: SAR; InSAR; time-series modeling; SAR CalVal
Special Issues, Collections and Topics in MDPI journals
Interests: SAR; InSAR; PSInSAR; geophysical modeling; volcanoes; landslides; geohazards
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on discussing research that utilizes state-of-the-art deep learning techniques from optical and synthetic aperture radar (SAR) imagery to monitor land use and land cover changes. In recent years, evolving deep learning techniques have shown excellent results in analyzing optical and SAR images to accurately detect Earth's surface changes, which are caused by various factors such as natural and anthropogenic hazards and environmental changes. Deep learning research in land use and land cover change monitoring emphasizes the richness and diversity of training data and models trained in various geographical regions and environmental conditions. Through the innovative use of deep learning technology, global change detection, which was previously considered a difficult or expensive task, can be performed efficiently. For this Special Issue, we invite innovative research that uses deep learning techniques to accurately monitor various changes in land use and land cover using optical and SAR imagery.
Prof. Dr. Hyung-Sup Jung
Dr. Jin-Woo Kim
Prof. Dr. Zhong Lu
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing
- change detection
- earth surface changes
- geohazards
- time series analysis
- deep learning
- optical/infrared imagery
- SAR/InSAR imagery
- land use/land cover
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