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State of the Art in Land Cover Classification and Mapping: Building Up Digital Twins of Earth

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing for Geospatial Science".

Deadline for manuscript submissions: 15 March 2027 | Viewed by 2636

Editors

Prof. Dr. Jingxiong Zhang

E-Mail Website
Guest Editor
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: geostatistics; remote sensing; digital terrain analysis; vegetation mapping; land cover
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Won-Ho Nam

E-Mail Website
Guest Editor
School of Social Safety and Systems Engineering, Hankyong National University, Anseong 17579, Republic of Korea
Interests: irrigation and drainage engineering; agricultural drought and water resource management; drought monitoring, mitigation, planning, and policy; risk and vulnerability management; remote sensing for drought monitoring and management; soil moisture and hydrologic/watershed modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Wangle Zhang

E-Mail Website
Guest Editor
Hubei Water Resources Research Institute, Wuhan 430070, China
Interests: remote sensing; land cover, machine learning

Special Issue Information

Dear Colleagues,

Land cover classification and mapping have advanced significantly, methodologically and technically, with ongoing improvements in conceptualization, models and methods, remote sensing science and systems, instrumentation, computer algorithms, and implementations. However, due to complexity in semantics, scale, phenology, and other characteristics inherent to land cover, many challenging issues remain. Certain cover types (e.g., vegetation) are more difficult to classify and map than others (e.g., water bodies). Inconsistency and variability are common, even for experienced image analysts. Scientific rigor, technical sophistication, higher accuracy, and cost-effectiveness are crucial, as are guidelines and overviews of good practice in these areas.

This Special Issue (SI), revised from its earlier theme on land cover classification and mapping, aims to bring together multidisciplinary scientists and specialists to advance research on concepts, models, methods, algorithms, and practicalities concerning land cover classification and mapping. Key strategic research areas will be thoroughly discussed to identify bottlenecks to breakthroughs. This SI will facilitate communication among researchers and practitioners on topics of mutual interest. Topics of interest include, but are not limited to, the following:

  • Classification system, harmonization, interoperability, and standards;
  • Semantics and thematic resolution;
  • Conceptualization of land cover as fields vs. objects;
  • Multi-resolution, proportional, and fuzzy representations of land cover;
  • Models of scale and minimum mapping units (MMUs);
  • Upscaling and downscaling;
  • Sampling design for reference data acquisition;
  • Image interpretation, interpreter variability, consistency, and quality assurance;
  • Training datasets for machine learning oriented for land cover mapping;
  • Spectral, spatial, and temporal features and their informativeness;
  • Phenology and time series analysis;
  • Statistical vs. rule-based classification methods;
  • Physics-informed and explainable machine learning in land cover classification and mapping;
  • Fusion of sensors, data, features, and classifiers;
  • Data cubes of existing land cover products and land cover primitives;
  • Well-targeted re-mapping of land cover;
  • Accuracy metrics and assessments for pixels, classes, and all problem domains;
  • Uncertainty characterization;
  • Thematic and regional case studies of cropland, grassland, shrub, forest, wetland, impervious surfaces, water bodies, and other broad cover types;
  • Best practice in land cover classification and mapping.

Prof. Dr. Jingxiong Zhang
Prof. Dr. Won-Ho Nam
Dr. Wangle 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.

Keywords

  • classification
  • mapping
  • classification systems
  • land cover
  • remote sensing images
  • scale
  • resolution
  • semantics
  • spectral–spatial–temporal features
  • phenology
  • pattern analysis
  • machine learning
  • rule bases
  • accuracy metrics and assessment
  • uncertainty
  • confusion matrix
  • mixed pixels
  • sampling
  • reference samples
  • image interpretation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 6052 KB  
Article
An Uncertainty-Aware Hybrid CNN–Transformer Network for Accurate Water Body Extraction from High-Resolution Remote Sensing Images in Complex Scenarios
by Qiao Xu, Huifan Wang, Pengcheng Zhong, Yao Xiao, Yuxin Jiang, Yan Meng, Qi Zhang, Cheng Zeng, Yangjie Sun and Yuxuan Liu
Remote Sens. 2026, 18(8), 1210; https://doi.org/10.3390/rs18081210 - 17 Apr 2026
Cited by 1 | Viewed by 772
Abstract
Timely and accurate monitoring of surface water dynamics via remote sensing is critical, given water resources’ importance. However, accurate water body delineation based on high-resolution remotely sensed imagery is still challenging due to the complexity of water bodies’ boundaries and the diversity of [...] Read more.
Timely and accurate monitoring of surface water dynamics via remote sensing is critical, given water resources’ importance. However, accurate water body delineation based on high-resolution remotely sensed imagery is still challenging due to the complexity of water bodies’ boundaries and the diversity of their shapes and sizes, which can lead to boundary ambiguity and varying degrees of confusion with near-water vegetation in water body maps. To address this challenge, we introduce an uncertainty-aware hybrid CNN–Transformer model for delineating water bodies using remotely sensed imagery. In our designed network, a multi-scale transformer (MST) module is first designed to effectively model and refine the multi-scale global semantic dependencies of water bodies. Subsequently, an uncertainty-guided multi-scale information fusion (MSIF) module is constructed to extract water body mapping information from these multi-scale features output from the MST module and fuse them adaptively. Across different scales, the extracted features differ in their ability to distinguish water bodies from non-water bodies and in their levels of uncertainty. Consequently, during the adaptive fusion of multi-scale water body information in the MSIF module, the mapping uncertainty is quantified and suppressed to minimize its impact, thus yielding enhanced precision in water body delineation. Ultimately, a comprehensive loss function is designed for model optimization to generate the final water body map. Furthermore, to promote water body segmentation models’ development, this study also presents the HBD_Water water body sample dataset, which contains 44 multispectral, 5000 × 5000-pixel images at 2 m spatial resolution, and will be released on the LuojiaSET platform soon. Finally, to verify the proposed model and its constituent MST and MSIF modules, extensive water mapping experiments were performed on three datasets. The experimental results substantiate their effectiveness. Furthermore, comparative experiment results demonstrate that the proposed model performs better at water body extraction than advanced networks including TransUNet, DeeplabV3+, and ADCNN. Full article
(This article belongs to the Special Issue State of the Art in Land Cover Classification and Mapping: Building Up Digital Twins of Earth)
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32 pages, 6874 KB  
Article
Advanced Semi-Supervised Learning for Remote Sensing-Based Land Cover Classification in the Mekong River Delta, Vietnam
by Hai-An Bui, Chih-Hua Hsu, Hsu-Wen Vincent Young, Yi-Ying Chen and Yuei-An Liou
Remote Sens. 2026, 18(7), 989; https://doi.org/10.3390/rs18070989 - 25 Mar 2026
Cited by 1 | Viewed by 1158
Abstract
The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to [...] Read more.
The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging. Full article
(This article belongs to the Special Issue State of the Art in Land Cover Classification and Mapping: Building Up Digital Twins of Earth)
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