Land Cover Classification of Multi-Source Remote Sensing Data Based on Deep Learning
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 84
Special Issue Editors
Interests: remote sensing image processing; classification; change detection
Interests: image analysis; remote sensing image processing; target recognition and change detection
Interests: remote sensing image processing; classification; change detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Earth observation has evolved from single-modality sensing to a multi-dimensional paradigm that integrates spatial, temporal, and spectral information, as well as semantic, across heterogeneous data sources. Driven by advances in AI and Earth observation capabilities, recent studies have moved beyond conventional multimodal fusion by aligning and unifying diverse sensor data (e.g., optical, SAR, LiDAR, hyperspectral data), substantially enhancing cross-modal understanding and downstream application performance. In addition, the development of language–visual models demonstrates the ability to integrate multimodal data and text information such as semantics and geospatial knowledge, which may form a new multimodal data utilization pattern and promote more accurate and efficient relevant applications.
This Special Issue focuses on how to leverage deep learning methods, including large language models (LLMs) and language–visual models (LVMs) to enable effective fusion of multi-source Earth observation data oriented to real-world application scenarios. It aims to address the challenges of Earth perception and cognition in the context of multi-dimensional observation, and to better serve the observational needs of key domains such as agriculture, forestry, water resources, and soil science.
Topics of interest include, but are not limited to, the following:
- Advanced deep learning annotation methods for multi-source remote sensing datasets;
- Challenges in multi-source remote sensing data processing (e.g., registration, fusion, alignment);
- Multi-source remote sensing dataset for classification/change detection/time series analysis;
- Multi-source remote sensing data for agricultural classification/change detection;
- Multi-source remote sensing data for forestry classification/change detection;
- Multi-source remote sensing data for grassland classification/change detection;
- Multi-source remote sensing data for water body classification/change detection;
- Multi-source remote sensing data for soil classification/change detection;
- Multi-source remote sensing data for land cover classification/change detection;
- Language–vision models for multi-source remote sensing data integration and applications;
- Multi-source remote sensing data for time series changing analysis;
- Data engineering of multi-source remote sensing.
Dr. Xiaofei Mi
Dr. Zhong Chen
Prof. Dr. Jian Yang
Dr. Zhijing Ye
Guest Editors
Manuscript Submission Information
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Keywords
- multi-source remote sensing data
- deep learning
- datasets
- classification
- change detection
- time-series change analysis
- language-visual models
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