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Remote Sensing
Special Issues
AI-Enhanced Remote Sensing for High-Precision Positioning and Navigation
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AI-Enhanced Remote Sensing for High-Precision Positioning and Navigation

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 2287

Editors

Prof. Dr. Xiangdong An

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: global navigation satellite systems; multi-sensor fusion for positioning and attitude determination
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaolin Meng

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: intelligent mobility; GNSS structural healthy monitoring; brain-inspired navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Craig M. Hancock

E-Mail Website
Guest Editor
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: characterization and mitigation of errors on GNSS; structural and natural change monitoring; spatial data acquisition for BIM and digital construction
Special Issues, Collections and Topics in MDPI journals
Dr. Qusen Chen

E-Mail Website
Guest Editor
GNSS Research Center, Wuhan University, Wuhan 430079, China
Interests: GNSS precise positioning; deformation monitoring; deformation time series analysis

Special Issue Information

Dear Colleagues,

Continuous high-precision positioning and navigation services play a vital role in intelligent transportation, geosciences, infrastructure monitoring, and emerging autonomous systems. With the rapid advancement of artificial intelligence (AI), machine learning, and data-driven methods, new opportunities have emerged to enhance positioning and navigation performances, and address long-standing limitations. By fusing diverse remote sensing observations, such as Global Navigation Satellite Systems (GNSS), optical imagery, LiDAR, and radar, with AI-enhanced algorithms, it becomes possible to overcome limitations caused by multipath effects, signal blockages, and dynamic environments, thus paving the way for more robust and intelligent positioning solutions.

This Special Issue aims to bring together cutting-edge research on AI-enhanced remote sensing for high-precision positioning and navigation. The objective is to explore how AI and related remote sensing technologies can improve error modeling, real-time correction, sensor fusion, and performance in challenging environments. By connecting methodological advances with practical applications, this issue aligns with the scope of Remote Sensing, particularly in advancing geospatial technologies, intelligent sensing, and navigation solutions for both scientific and industrial domains.

We welcome a wide range of contributions, including original research papers, methodological developments, case studies, and comprehensive reviews. The suggested topics include, but are not limited to, the following:

  • AI-driven methods for error modeling, mitigation, and correction in remote sensing techniques
  • Machine learning approaches for multi-frequency, multi-constellation GNSS
  • AI-enhanced fusion of multiple data sources from remote sensing sensors
  • Intelligent positioning in urban, indoor, or harsh environments based on multi-sensor fusion
  • Real-time high-precision navigation for autonomous vehicles, UAVs, and maritime systems
  • AI-assisted atmospheric and ionospheric modeling for GNSS applications
  • Applications of deep learning and graph neural networks in precise positioning
  • The future development of AI-enhanced precision positioning and navigation and applications

Prof. Dr. Xiangdong An
Prof. Dr. Xiaolin Meng
Dr. Craig M. Hancock
Dr. Qusen Chen
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

  • intelligent high-precision positioning
  • AI-enhanced fusion of multiple remote sensing data sources
  • AI-enhanced navigation
  • machine learning for remote sensing techniques
  • GNSS/INS/vision fusion
  • GNSS error modeling and mitigation with AI

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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Research

24 pages, 6298 KB  
Article
Siamese-ViT: A Local–Global Feature Fusion Method for Real-Time Visual Navigation of UAVs in Real-World Environments
by Yu Cheng, Xixiang Liu, Shuai Chen and Chuan Xu
Remote Sens. 2026, 18(10), 1556; https://doi.org/10.3390/rs18101556 - 13 May 2026
Viewed by 381
Abstract
Visual scene matching navigation (VSMN) for unmanned aerial vehicles (UAVs) boasts advantages such as high precision, high reliability, and autonomy. The biggest challenge lies in the tension between local fine-grained information and global semantics, as well as limited generalization ability in real-world environments. [...] Read more.
Visual scene matching navigation (VSMN) for unmanned aerial vehicles (UAVs) boasts advantages such as high precision, high reliability, and autonomy. The biggest challenge lies in the tension between local fine-grained information and global semantics, as well as limited generalization ability in real-world environments. While existing Transformer-based cross-view geolocation methods enhance global context modeling capabilities, they still generally face issues such as high demands on training data and computational resources, insufficient fusion of local fine-grained information and global semantics, and real-time performance in real-world complex environment. To address these problems, we propose a scene matching and localization algorithm based on the Siamese-ViT. For feature extraction, we use the ViT model to extract global features and K-means clustering to aggregate local features. Combined with the global features extracted by the ViT, a robust local–global feature representation vector is generated. For feature matching, incremental principal component analysis (IPCA) is used to reduce the dimensionality of the high-dimensional feature space, and a KD-tree is constructed for fast feature retrieval to improve matching efficiency. We validated our algorithm on the University-1652 dataset and a dataset of real-world satellite-drone image pairs. The results show that our Siamese-ViT outperforms other models in both Recall and AP. We conduct flight experiments in real-world environments, capturing drone images of complex scenes, including farmland, urban buildings, and waterways. The results show that, at a flight altitude of 350 m, our algorithm achieves an average absolute value of 6.2063 m for latitude, 6.7552 m for longitude, and 10.1922 m for horizontal error. Therefore, our Siamese-ViT demonstrates ideal overall positioning accuracy. Full article
(This article belongs to the Special Issue AI-Enhanced Remote Sensing for High-Precision Positioning and Navigation)
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27 pages, 10846 KB  
Article
A Multimodal Feature Fusion Framework for UAV Positioning in Weak GNSS Environments Using a Priori High-Resolution Satellite Imagery
by Liming He, Zhengqi Zhao, Zhenglin Qu, Ronghua He, Yu Zhang, Haoran Li and Yadong Zhu
Remote Sens. 2026, 18(5), 752; https://doi.org/10.3390/rs18050752 - 2 Mar 2026
Viewed by 1240
Abstract
To address the challenges of unmanned aerial vehicle (UAV) navigation in weak global navigation satellite system (GNSS) environments, this study proposes a novel multimodal feature fusion framework for real-time positioning using a priori high-resolution satellite imagery. This framework utilizes georeferenced satellite images as [...] Read more.
To address the challenges of unmanned aerial vehicle (UAV) navigation in weak global navigation satellite system (GNSS) environments, this study proposes a novel multimodal feature fusion framework for real-time positioning using a priori high-resolution satellite imagery. This framework utilizes georeferenced satellite images as matching sources and employs a “Multimodal features + LightGlue” algorithm to achieve high-precision cross-modal matching. By combining point, line, and plane features for enhanced robustness in low-texture scenarios, the system further integrates LightGlue’s lightweight confidence classifier to accelerate inference while maintaining high accuracy on challenging image pairs. Consequently, the proposed method outperforms LoFTR, RoMa, SuperPoint + SuperGlue, and SuperPoint + LightGlue in matching performance. Experimental results demonstrate that at a flight altitude of 80 m, the average real-time positioning error is 0.73 m, which increases to 6.24 m at 480 m. Factors such as ground object type, seasonal changes, flight altitude, and satellite image scale significantly influence accuracy. This research demonstrates that the visual navigation system meets practical operational needs for real-time UAV positioning in GNSS-deprived environments. Full article
(This article belongs to the Special Issue AI-Enhanced Remote Sensing for High-Precision Positioning and Navigation)
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