New Advances in Remote Sensing Data Mining and Knowledge-Guided Machine Learning

Dear Colleagues,

The rapid growth of Earth observation systems has led to an unprecedented increase in the volume, diversity, and complexity of remote sensing data. Satellite constellations, airborne platforms, and multi-sensor networks continuously generate large-scale multimodal datasets, including optical, SAR, hyperspectral, microwave, and LiDAR observations. Extracting reliable, scalable, and interpretable knowledge from these heterogeneous data sources has become a central challenge in modern remote sensing science.

Recent advances in data mining and machine learning, particularly knowledge-guided learning, self-supervised learning, and foundation models, have opened new opportunities for intelligent Earth observation. By integrating physical models, domain knowledge, and structured information into data-driven frameworks, knowledge-guided machine learning improves generalization, robustness, and interpretability. These developments are transforming remote sensing from traditional image processing into knowledge-driven geospatial intelligence.

This Special Issue aims to present recent advances in remote sensing data mining and knowledge-guided machine learning methodologies that address emerging challenges in Earth observation. It seeks contributions that bridge data-driven modeling and domain knowledge integration to enhance information extraction, analysis, and decision support from remote sensing data.

The topic is fully aligned with the scope of Remote Sensing, particularly in areas related to geospatial artificial intelligence, remote sensing data processing, environmental monitoring, and advanced computational methods for Earth observation.

Topics of interest include, but are not limited to, the following:

• Knowledge-guided and physics-informed machine learning for remote sensing
• Advanced data mining techniques for large-scale Earth observation
• Self-supervised and foundation models for geospatial data
• Vision–language and multimodal large models for Earth observation
• Multisource and multimodal remote sensing data fusion
• Spatiotemporal modeling and change detection
• Explainable and trustworthy AI for remote sensing
• Distributed and collaborative Earth observation
• Applications in climate monitoring, disaster management, agriculture, urban studies, and environmental assessment

We welcome original research articles, review papers, and methodological contributions that advance theory, algorithms, and applications in this rapidly evolving field.

Dr. Congcong Wen
Dr. Xidong Chen
Dr. Ying Zhang
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.