Big Earth Data Journal: Addressing incomplete reference data for leveraging Artificial Intelligence in Earth Observation applications

Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth’s systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.

Currently, Big Earth Data is calling for papers for a Special Issue on Addressing incomplete reference data for leveraging Artificial Intelligence in Earth Observation applications. 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.

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.

Submission Instructions

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. Publication charges (APCs) may be waived for invited manuscripts submitted to Big Earth Data, subject to approval based on their quality and potential impact. Authors who need a waiver code should contact the Editorial Office (guanll@aircas.ac.cn) before submitting.

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