Remote Sensing Training Data: Annotation, Quality, and Optimization
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 28
Special Issue Editors
Interests: GeoAI; spatial data provenance; training data quality
Special Issues, Collections and Topics in MDPI journals
Interests: GIScience; spatial computing; GeoAI; VGI; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing intelligent interpretation; data quality; GeoAI applications
Special Issue Information
Dear Colleagues,
High-quality training data is fundamental to the development of accurate and reliable remote sensing models. As remote sensing technologies and applications expand rapidly—ranging from environmental monitoring to urban planning and disaster management—the demand for precise and well-annotated datasets grows. However, challenges such as label noise, annotation inconsistencies, and suboptimal data quality remain major bottlenecks that hinder model performance and generalization. Addressing these issues through advanced annotation methodologies, noise mitigation techniques, and data optimization strategies is crucial for pushing the boundaries of remote sensing research and applications.
This Special Issue aims to gather recent developments and practical solutions related to training data annotation and quality enhancement in remote sensing. We welcome contributions on automated or AI-assisted annotation techniques, approaches to detect and correct label noise, methods to evaluate and improve data quality, and strategies for optimizing datasets to boost model performance. These topics fit well within Remote Sensing’s focus on novel data processing and analysis techniques.
We invite original research articles, comprehensive reviews, and case studies covering, but not limited to, the following themes:
- AI and machine learning approaches for automated data annotation;
- Techniques for identifying and mitigating noisy or incorrect labels;
- Methods and metrics for assessing training data quality;
- Data optimization frameworks to enhance model robustness and accuracy;
- Case studies demonstrating the impact of improved training data quality;
- Integrated workflows combining annotation and quality management.
Dr. Liangcun Jiang
Dr. Hao Li
Guest Editors
Dr. Boyi Shangguan
Guest Editor Assistant
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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind 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
- remote sensing training data
- training data quality
- data annotation
- label noise
- data quality assessment
- automated labeling
- label correction
- training data optimization
- quality control in remote sensing
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