Recent Progress in Land Cover Mapping Using Remote Sensing Data

Dear Colleagues,

Land cover mapping with remote sensing data has been an important research topic. Currently, researchers easily have access to many different data choices and combinations: high spatial resolution images (e.g., Planet imagery), multi-modal data (e.g., optical, hypespectral data, SAR, airborne LiDAR), and time series data (e.g., Landsat Archive, Sentinel Archive). However, these varied data pose many challenges for land cover mapping techniques, such as efficiently defining large quantities of training samples and jointly exploring multi-modal data for more accurate land cover maps. Recently, the rapid development in deep learning has provided new options to tackle these challenges: from data augmentation to different variants of deep models, and recently even popular foundation models. In this Special Issue, we invite submissions related to the recent progress in land cover mapping with remote sensing data, with topics including but not limited to:

Training data augmentation;

Multi-modal data classification;

Time series data analysis;

Transfer learning;

Self-supervised learning;

Remote sensing foundation models;

Accuracy assessment of land cover products.

Dr. Lian-Zhi Huo
Dr. Yuanwei Qin
Guest Editors

Manuscript Submission Information

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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. Land is an international peer-reviewed open access monthly 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 2600 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.

• multi-modal data classification
• time series data analysis
• transfer learning
• self-supervised learning
• accuracy assessment