专刊征稿|AI赋能对地观测中参考数据不完整问题的应对策略
作者：微信文章01
专刊缘起
Artificial Intelligence (AI) has been, and will continue to be, widely applied in aerial and satellite imaging data processing for various Earth observation (EO) tasks, including environmental monitoring, climate change analysis, and agricultural assessment. However, these AI-driven applications rely heavily on the quality and quantity of available reference data to support model training and validation. Despite the critical importance of reference data in big EO data processing, several challenges often arise, such as data inconsistencies, limited quantities, incompleteness, and temporal degradation. These challenges collectively undermine the reliability of the outputs and the overall processing chain. Therefore, addressing and mitigating these reference data issues is essential for optimizing and enhancing EO-based products.
Recognizing the increasing reliance on AI in EO, this special issue focuses on advancing methodologies for addressing and managing reference data challenges in EO applications. It brings together pioneering research, theoretical insights, and innovative case studies to promote advancements in reference data enhancement and refinement. By tackling these challenges and proposing innovative solutions, this special issue aims to improve the precision and trustworthiness of AI-driven insights, providing valuable guidance for academics, industry professionals, and policymakers.
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征文主题
Potential topics include (but are not limited to) the following:
1. Techniques for generating synthetic data to address limited training samples;
2. Robust models for handling incomplete or unreliable reference data;
3. Strategies for transferring knowledge from well-annotated datasets to others;
4. Optimizing available reference data by selecting the most informative samples;
5. Techniques for training models on imbalanced or incomplete datasets;
6. Using physical constraints to guide learning in data-limited scenarios;
7. Identifying and addressing biases introduced by imperfect reference data;
8. Efficient workflow for spatially and/or temporally transferring available reference data;
9. Developing benchmarks for evaluating models trained on incomplete reference data.
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提交须知
Important Dates
1 March 2026: Deadline for paper submission online
1 May 2026: Decision to authors
1 July 2026: Revised paper submission
1 September 2026: Publication
Manuscript Submission Information
Please visit the Instructions for Authors page before submitting your manuscript. Once you have finished preparing your manuscript, please submit it through the Taylor & Francis Submission Portal, ensuring that you select the appropriate Special Issue.
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客座主编
Dr. Hamid EbrahimyOsnabrück University, Germany
Email: hamid.ebrahimy@uni-osnabrueck.de
Dr. Amin NabourehInstitute of Mountain Hazards and Environment, CAS
Email: aminnaboureh@imde.ac.cn
Prof. Björn Waske
Osnabrück University, Germany
Email: bjoern.waske@uni-osnabrueck.de
Dr. Ali Jamali
Simon Fraser University, Canada
Email: alij@sfu.ca
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地球大数据国际期刊
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