Demand-Driven and Explainable Artificial Intelligence for Remote Sensing Data Mining and Applications
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 43
Editors
Interests: remote sensing image interpretation; agricultural remote sensing; crop classification; change detection; geospatial artificial intelligence
Interests: remote sensing; agricutlure; biogeophysical properties estimation; crop phenology; data assimilation
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; hyperspectral data processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of multi-source Earth observation technologies, including satellite sensors, unmanned aerial vehicles (UAVs), and LiDAR systems, the volume, diversity, and spatial-temporal coverage of remote sensing data have increased substantially. These advances have greatly enhanced capabilities for natural resource and environmental monitoring, assessment, and decision-making. However, significant challenges remain in demand-oriented data mining, task-specific heterogeneous data fusion, and large-scale data interpretation. Although AI-based models, along with high-performance computing and cloud platforms, have significantly advanced remote sensing image classification and data analysis, limitations persist in model interpretability, transferability across regions and sensors, and application-oriented information extraction. Consequently, this Special Issue will highlight the latest developments in demand-driven and interpretable artificial intelligence methods and their applications, aiming to enhance the reliability and operation of remote sensing methods in agriculture, ecosystem monitoring, disaster management, urban studies, and related fields.
We welcome original research articles on novel methods and applications involving machine learning, multimodal data fusion, knowledge-driven approaches, foundation models, and explainable artificial intelligence (XAI) for remote sensing image analysis and intelligent interpretation.
Topics of interest include, but are not limited to:
- Demand-driven remote sensing data mining and intelligent processing methods.
- Multi-source remote sensing data fusion and intelligent representation learning.
- Data mining algorithms and model optimization strategies for remote sensing analysis.
- Feature extraction, geobiophysical parameter retrieval, quantitative remote sensing, and uncertainty analysis.
- Explainable and trustworthy AI for remote sensing information extraction and decision support.
- Knowledge-guided learning, foundation models, and self-supervised approaches for remote sensing.
- Application-oriented remote sensing intelligence for agriculture, forestry, ocean and coastal monitoring, disasters, and urban environments.
Dr. Mengxi Liu
Dr. Taifeng Dong
Dr. Xuanwen Tao
Dr. Shengjie Liu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- data mining
- deep learning
- explainable artificial intelligence
- demand-driven remote sensing
- multi-source data fusion
- machine learning
- foundation models
- earth observation
- intelligent applications
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