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Advanced AI Technology for Remote Sensing Analysis (Second Edition)
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Advanced AI Technology for Remote Sensing Analysis (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2484

Editors

Dr. Guangliang Cheng

E-Mail Website
Guest Editor
Department of Computer Science, University of Liverpool, Liverpool, UK
Interests: computer vision; remote sensing; change detection; hyperspectral image classification; road extraction
Special Issues, Collections and Topics in MDPI journals
Dr. Shuchang Lyu

E-Mail Website
Guest Editor
School of Electronic Information Engineering, Beihang University, Beijing, China
Interests: compact CNN design; remote sensing scene image recognition; transfer learning and domain adaptation
Special Issues, Collections and Topics in MDPI journals
Dr. Hossein M. Rizeei

E-Mail Website
Guest Editor
BioUrbanism Lab, McGregor Coxall Australia Pty Ltd., Sydney, NSW 2095, Australia
Interests: GeoAI; machine learning; geospatial 3D analysis; geospatial intelligence; airborne/spaceborne image processing; feature extraction; predictive modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of our previous Special Issue, “Advanced AI Technology for Remote Sensing Analysis”, we are happy to announce that a new edition has been created.

The rapid evolution of artificial intelligence (AI) has precipitated remarkable advancements across various scientific and engineering domains. Among these, remote sensing stands out as a critical area where AI technologies, including machine learning and deep learning, are increasingly integrated to revolutionize how we acquire, process and interpret Earth observation data. Despite the inspiring success of previous work based on standard benchmark datasets, new advanced AI technologies still need to be further clarified and real-world downstream tasks still need to be further explored. This Special Issue aims to explore the cutting-edge intersections of AI technology and remote sensing analysis, presenting novel research findings, methodological developments and comprehensive reviews.

This Special Issue aims to investigate the application of advanced AI technologies for remote sensing analysis. We invite submissions that address various aspects of remote sensing analysis and the corresponding advanced AI technologies in remote sensing scenes. Potential topics of interest include, but are not limited to, the following:

  • Multi-modality (multi-source)image fusion;
  • Cross-domain adaptation or multi-domain generalization methods;
  • Multi-modality remote sensingimage benchmark datasets;
  • Multi-modality large foundation models for remote sensing scenes;
  • Visual–linguistic models for remote sensing scenes;
  • Case studies and applications of multi-modality networks in remote sensing for agriculture, urban planning, forestry, climate change, etc.;
  • Multi-view joint analysis of remote sensing scenes;
  • Image segmentation/detection or change detection;
  • Remote sensing analysis with foundation models.

Authors are invited to submit original research articles  (including reviews, articles, technical notes and communications) that contribute to the field of advanced AI for remote sensing. All submissions will undergo a rigorous peer-review process to ensure the quality and relevance of accepted papers. Manuscripts should follow the guidelines provided by the journal and should clearly address the Special Issue theme.

Dr. Guangliang Cheng
Dr. Shuchang Lyu
Dr. Hossein M. Rizeei
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

  • multi-modality images
  • image fusion
  • cross-domain adaptation
  • visual–linguistic models
  • multi-modality downstream tasks
  • remote sensing
  • pattern analysis
  • advanced AI technology
  • image segmentation/detection and change detection

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

Related Special Issue

Published Papers (4 papers)

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Research

37 pages, 4767 KB  
Article
A Vertically Structured Machine Learning Approach for Cloud Liquid and Ice Water Content Profiling
by Zhengyu Pan, Yansong Bao, Hong Wei, Haoran Li, Fang Pang and Wei Tao
Remote Sens. 2026, 18(13), 2177; https://doi.org/10.3390/rs18132177 - 3 Jul 2026
Viewed by 275
Abstract
Accurate retrieval of cloud liquid water content (LWC) and ice water content (IWC) vertical profiles remains limited by strong vertical variability and nonlinear dependencies among observed variables. Ground-based cloud radar reflectivity and microwave radiometer-derived thermodynamic profiles provide complementary constraints, but their joint use [...] Read more.
Accurate retrieval of cloud liquid water content (LWC) and ice water content (IWC) vertical profiles remains limited by strong vertical variability and nonlinear dependencies among observed variables. Ground-based cloud radar reflectivity and microwave radiometer-derived thermodynamic profiles provide complementary constraints, but their joint use requires consistent time–height matching and bias-controlled predictors. This study develops a vertically structured machine-learning framework that explicitly represents profile-level dependencies by constructing vertical-structure-enhanced features to encode local gradients and contextual information, integrating multiple tree-based learners with heterogeneous configurations through a profile-aware stacking strategy, and introducing a profile-level refinement step to suppress layer-to-layer inconsistencies. The framework is evaluated using year-round Cloudnet observations from the Lindenberg site, where IWC RMSE decreases from 0.0152 g m−3 to 0.0092 g m−3 with R2 increasing from 0.412 to 0.784, and LWC RMSE decreases from 0.0786 g m−3 to 0.0591 g m−3 with R2 increasing from 0.303 to 0.606. Additional boundary-region evaluation shows that the improvement is particularly evident near radar-derived cloud boundaries, where cloud structure and hydrometeor content may vary rapidly with height. These results indicate that treating cloud retrieval as a vertically structured learning problem reduces inconsistencies inherent in pointwise models and establishes a data-driven baseline for incorporating vertical constraints into atmospheric profile retrieval. Full article
(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis (Second Edition))
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25 pages, 22795 KB  
Article
MSDR-Net: Multiscale Dynamic Reasoning for Multi-Label Remote Sensing Image Classification
by Qinghe Sun, Hua Wang, Shuai Wang, Teng Yang, Hui Zhao and Xuewu Fan
Remote Sens. 2026, 18(11), 1798; https://doi.org/10.3390/rs18111798 - 1 Jun 2026
Viewed by 576
Abstract
With the rapid advancement of Earth observation technologies and the growing demand for intelligent remote sensing applications, high-resolution remote sensing imagery provides critical data support for a range of downstream applications, including land monitoring and disaster assessment. In this context, multi-label remote sensing [...] Read more.
With the rapid advancement of Earth observation technologies and the growing demand for intelligent remote sensing applications, high-resolution remote sensing imagery provides critical data support for a range of downstream applications, including land monitoring and disaster assessment. In this context, multi-label remote sensing image classification has become an important research task, because a single image may contain multiple ground-object categories with complex spatial distributions and semantic co-occurrence relationships. However, challenges such as the coexistence of multiscale objects, complex semantic dependencies, and long-tail category distributions impose significant limitations on existing methods in terms of feature representation capacity and class-balanced modeling. To address these challenges, a Multiscale Dynamic Reasoning Network (MSDR-Net) is proposed. Different from methods that focus on localized optimization for a single challenge, MSDR-Net establishes a task-driven modeling framework that jointly integrates multiscale feature extraction, label-aware semantic reasoning, and long-tail category optimization within an end-to-end architecture. The proposed network consists of three core modules. The Multiscale Feature Enhancement (MSFE) module incorporates a Feature Pyramid Network-based fusion mechanism, integrating deep semantic information with shallow, detailed features to effectively enhance the representation of multiscale objects. The Dynamic Semantic Reasoning (DSR) module introduces a Transformer-based global attention mechanism that models long-range dependencies among image features, enabling the capture of complex global semantic relationships. In the loss optimization stage, a Difficulty-Weighted Loss (DW-Loss) is introduced, which jointly incorporates category frequency weights and prior difficulty coefficients to dynamically regulate the contributions of rare classes and hard samples during training, thereby mitigating bias induced by class imbalance. Experiments conducted on the large-scale Detection in Optical Remote Sensing Images dataset demonstrate that the proposed method achieves superior performance. Ablation studies validate the effectiveness of each component, while comparative experiments indicate that MSDR-Net achieves a mean Average Precision of 95.88%, outperforming existing state-of-the-art methods. An improvement of approximately 1.74% is observed over the strongest baseline, MSCA, with consistent advantages demonstrated across Overall F1 and Class-wise F1 metrics. By unifying multiscale feature extraction, global semantic reasoning, and balanced loss optimization within a single framework, MSDR-Net provides a robust and efficient solution for multi-label classification in complex remote sensing scenarios. Full article
(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis (Second Edition))
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30 pages, 22156 KB  
Article
Daily-Scale Meteorological Normalization of Surface Solar Radiation in Varying Pollution Levels: A Statistical Case Study in Beijing (2015–2019)
by Tong Wu, Zhigang Li and Xueying Zhou
Remote Sens. 2026, 18(9), 1368; https://doi.org/10.3390/rs18091368 - 29 Apr 2026
Viewed by 533
Abstract
Surface solar radiation at the ground is affected by aerosols, clouds, and atmospheric moisture, as well as by circulation-related conditions that influence cloud formation and pollutant transport. In daily observations, these influences are mixed, which makes pollution-related variability difficult to interpret. We analyzed [...] Read more.
Surface solar radiation at the ground is affected by aerosols, clouds, and atmospheric moisture, as well as by circulation-related conditions that influence cloud formation and pollutant transport. In daily observations, these influences are mixed, which makes pollution-related variability difficult to interpret. We analyzed data from Beijing station 54511 (2015–2019), including daily integrated radiation components and collocated meteorological and pollution variables. We used wavelet coherence, pollution-stratified association analysis, and gray relational analysis, and compared two meteorological normalization methods: multiple linear regression (MLR) and random forest (RF). The results show that meteorological–radiation relationships vary systematically across pollution levels, indicating substantial meteorological confounding in daily radiation analyses. Among the radiation components, DR shows the clearest pollution-dependent shift in its relationship with RH, while several direct components become less sensitive to cloud cover under heavier pollution. RF reproduced daily radiation components with strong predictive performance (R2 = 0.83–0.88), and the meteorologically adjusted anomalies from RF were consistent with those from MLR (r = 0.63–0.78 across components). These findings suggest that both MLR and RF can be effectively used to normalize meteorological effects in daily station records. The analysis supports routine interpretation of day-to-day surface radiation variability and can be extended to multi-site studies and finer temporal resolution. Full article
(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis (Second Edition))
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30 pages, 5777 KB  
Article
CADF-Net: A Conflict-Aware Adaptive Distillation Network for Fusing Multi-Source Land-Cover Products for Key Vegetation Classes in Cross-Border Regions
by Yubo Zhang, Long Fu, Zehong Li, Yuanyuan Yang, Hongbing Chen and Shuwen Zhang
Remote Sens. 2026, 18(9), 1294; https://doi.org/10.3390/rs18091294 - 24 Apr 2026
Viewed by 537
Abstract
Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping [...] Read more.
Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping priorities, classification instability near national boundaries undermines transboundary comparisons. To address this, we propose a Conflict-aware Adaptive Distillation Fusion Network (CADF-Net) that fuses multi-source land-cover products to improve the discrimination and spatial consistency of key vegetation classes in cross-border regions. Taking the transnational China–Russia border (Sanjiang Plain and Primorskiy Kray) as a representative case, we integrate geo-environmental factors and introduce a pixel-level Conflict Index (CI) to explicitly steer the model toward discrepancy-prone areas. Building on this, we develop an Adaptive Distillation U-Net (AD-UNet) with uncertainty-adaptive distillation and employ a confidence-guided, dynamically weighted ensemble to generate the final fused land-cover product (CADF-LC). Quantitative assessments demonstrate that CADF-LC achieved an OA of 0.8600, a Kappa of 0.8133, and an mIoU of 0.7589, outperforming all input land-cover products. Compared with the strongest input product, Esri Land Cover, CADF-LC improved OA by 0.0150 and mIoU by 0.0222. Furthermore, it effectively mitigates the trade-off between detail loss and morphological fragmentation. Ultimately, CADF-Net enhances classification stability for key vegetation classes, offering a reliable foundation for transboundary ecological monitoring and land management. Full article
(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis (Second Edition))
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