Leveraging Multitemporal Remote Sensing Data for Land Use and Land Cover Classification
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 March 2026
Special Issue Editors
Interests: Remote Sensing; OBIA; Image Segmentation; CNNs, GIS; Land cover
Interests: Natural Hazards; Land Management; GeoAI; Sustainable Development; GIS; Remote Sensing
Interests: urban land use; machine learning/deep learning; thermal infrared remote sensing; urban heat island effect; urban green infrastructure; disaster risk assessment; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
The dynamic nature of Earth’s surface necessitates advanced methodologies for accurate and timely land use and land cover classification. Multitemporal remote sensing data, by capturing seasonal and phenological patterns, provide valuable information that is not discernible in single-date imagery. Moreover, leveraging sequences of observations helps mitigate issues of sensor noise, cloud cover gaps, and land‐surface variability, paving the way for robust, scalable workflows that keep pace with dynamic environmental change.
This Special Issue aims to consolidate cutting-edge research that leverages multitemporal remote sensing data for land use and land cover classification. We welcome contributions that integrate multitemporal remote sensing datasets with modern analytical techniques to enhance classification. Studies may employ a range of approaches, from traditional machine learning classifiers to advanced AI models with deep learning architectures, handling variability in data sources, temporal sampling, and spatial detail. Articles may address, but are not limited to, the following topics:
Algorithmic developments;
Advanced AI and deep learning models for analyzing multitemporal remote sensing data;
Integration of spectral indices;
Multimodal data fusion combining optical, SAR, and other remote sensing data;
Development of benchmark datasets and evaluation metrics for multitemporal land use and land cover classification;
Applications of multitemporal analysis in monitoring changes, urban expansion, deforestation, burned areas, mining sites, and agricultural dynamics.
Dr. Ioannis Kotaridis
Dr. Hazem Ghassan Abdo
Dr. Linlin Lu
Guest Editors
Manuscript Submission Information
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Keywords
- Land use and land cover classification
- Image classification
- Semantic segmentation
- Multitemporal
- Multimodal
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Deep Learning (DL)
- Geospatial Information Systems (GIS)
- Remote sensing
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