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

我爱免费

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.
02

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.

03

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.

04

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

05

其他专刊征稿

地理大数据中的不确定性研究：进展与挑战（截止日期：2025年12月1日）
土地利用与土地覆盖监测在推动可持续发展目标中的应用（截止日期：2025年12月1日）基于对地观测和地理空间人工智能的社会科学研究（截止日期：2025年12月31日）



  排版｜杜小冰

  编辑｜杨博锦

  校审｜关琳琳

  终审｜王长林

  地球大数据国际期刊

长按二维码关注我们



声明

欢迎转载、转发本号的原创推文，转载前请与本号联系授权，并标注原创作者和信息来源为《地球大数据国际期刊》。
本号转载推文旨在信息传播、交流与分享，其内容由原创作者负责，不代表本号观点。文中部分图片来源于网络，如涉及作品内容、版权和其他问题，请在15日内与本号联系，我们将在第一时间处理。《地球大数据国际期刊》拥有最终解释权。