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Remote Sensing
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Advances in Applications of Remote Sensing GIS and GNSS
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Advances in Applications of Remote Sensing GIS and GNSS

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

Deadline for manuscript submissions: 30 October 2025 | Viewed by 9538

Special Issue Editors

Dr. Bing Xu

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: signal processing; satellite positioning and navigation; GNSS receiver design; parameter estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Wang

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: GNSS positioning; GNSS remote sensing; atmosphere modeling; LEO navigation augmentation
Special Issues, Collections and Topics in MDPI journals
Dr. Qian (Chayn) Sun

E-Mail Website
Guest Editor
Geospatial Science, School of Science, RMIT University, Melbourne, Australia
Interests: spatial statistics and analysis; health geography; climate change adaptation; machine learning; urban spatial modelling; human perception and behaviour
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis Partsinevelos

E-Mail Website
Guest Editor
Senselab Research, School of Mineral Resources Engineering, Technical University of Crete, 73100 Chania, Greece
Interests: unmanned aerial systems; tangible GIS; GNSS-denied environments; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of geospatial information system (GIS), remote sensing (RS), and global navigation satellite system (GNSS) research, new theories, methods and applications have emerged, such as the concept of real-time GIS (RT-GIS), three-dimensional GIS (3D-GIS), indoor localization and mapping, machine-learning-enabled imagery interpretation and enhancement, advanced GNSS signal processing techniques, anti-jamming and anti-spoofing techniques, positioning and navigation with low-Earth-orbit satellites and cellular networks’ comprehensive positioning navigation and timing (PNT)-related techniques, etc. In addition, many new inter-disciplinary research topics have become popular, such as vision-based navigation, ground control point (GCP)-free photogrammetry, synthetic aperture radar satellite formation flying, satellite-image-based disaster monitoring systems, and resource management. The fusion of RS, GIS and GNSS techniques has also fostered many new applications, which help acquire spatial information faster and easier and manage spatial information more efficiently.

The scope of this Special Issue includes (but is not limited to) the following topics:

  • Satellite image processing theory and methods;
  • SAR image data process and applications;
  • LiDAR segmentation and cloud point process methods;
  • GCP-free photogrammetry methods;
  • Novel indoor localization method;
  • Real-time GIS relative topics;
  • 3D-GIS and future GIS techniques;
  • Monitoring of coastal ecology and human activities based on 3S technology;
  • Advanced GNSS signal processing and new GNSS positioning theory;
  • GNSS meteorology-related topics;
  • Multi-GNSS biases and geophysics inversion;
  • Time and frequency transfer-related topics;
  • Comprehensive PNT techniques;
  • The fusion of GIS, RS and GNSS techniques and the applications to environmental change detection, disaster resilience planning, monitoring land use and land cover changes, smart cities, etc.

Dr. Bing Xu
Dr. Lei Wang
Dr. Qian (Chayn) Sun
Dr. Panagiotis Partsinevelos
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. 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.

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

Published Papers (4 papers)

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23 pages, 23211 KiB  
Article
Efficient Path Planning Algorithm Based on Laser SLAM and an Optimized Visibility Graph for Robots
by Yunjie Hu, Fei Xie, Jiquan Yang, Jing Zhao, Qi Mao, Fei Zhao and Xixiang Liu
Remote Sens. 2024, 16(16), 2938; https://doi.org/10.3390/rs16162938 - 10 Aug 2024
Cited by 1 | Viewed by 2587
Abstract
Mobile robots’ efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the [...] Read more.
Mobile robots’ efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms. Full article
(This article belongs to the Special Issue Advances in Applications of Remote Sensing GIS and GNSS)
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23 pages, 10455 KiB  
Article
ULG-SLAM: A Novel Unsupervised Learning and Geometric Feature-Based Visual SLAM Algorithm for Robot Localizability Estimation
by Yihan Huang, Fei Xie, Jing Zhao, Zhilin Gao, Jun Chen, Fei Zhao and Xixiang Liu
Remote Sens. 2024, 16(11), 1968; https://doi.org/10.3390/rs16111968 - 30 May 2024
Cited by 7 | Viewed by 1707
Abstract
Indoor localization has long been a challenging task due to the complexity and dynamism of indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. [...] Read more.
Indoor localization has long been a challenging task due to the complexity and dynamism of indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. Firstly, a dynamic feature filtering based on unsupervised learning and moving consistency checks is developed to eliminate the features of dynamic objects. Secondly, an improved line feature extraction algorithm based on LSD is proposed to optimize the effect of geometric feature extraction. Thirdly, geometric features are used to optimize localizability estimation, and an adaptive weight model and attention mechanism are built using the method of region delimitation and region growth. Finally, to verify the effectiveness and robustness of localizability estimation, multiple indoor experiments using the EuRoC dataset and TUM RGB-D dataset are conducted. Compared with ORBSLAM2, the experimental results demonstrate that absolute trajectory accuracy can be improved by 95% for equivalent processing speed in walking sequences. In fr3/walking_xyz and fr3/walking_half, ULG-SLAM tracks more trajectories than DS-SLAM, and the ATE RMSE is improved by 36% and 6%, respectively. Furthermore, the improvement in robot localizability over DynaSLAM is noteworthy, coming in at about 11% and 3%, respectively. Full article
(This article belongs to the Special Issue Advances in Applications of Remote Sensing GIS and GNSS)
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Other

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16 pages, 3949 KiB  
Technical Note
Precision Analysis of Multi-Parameter Multi-Epoch Emitter Localization Radar in Three-Satellite Formation
by Yiming Lian, Yuxuan Wu, Yaowen Chen, Xian Liu and Liming Jiang
Remote Sens. 2025, 17(1), 96; https://doi.org/10.3390/rs17010096 - 30 Dec 2024
Viewed by 615
Abstract
Emitter localization offers significant advantages such as high concealment, long detection range, and low cost, making it indispensable in target positioning. The utilization of low earth orbit satellite formation with AOA (Angle of Arrival) and TDOA (Time Difference of Arrival) measurements is a [...] Read more.
Emitter localization offers significant advantages such as high concealment, long detection range, and low cost, making it indispensable in target positioning. The utilization of low earth orbit satellite formation with AOA (Angle of Arrival) and TDOA (Time Difference of Arrival) measurements is a key technology for achieving emitter localization. To address the issues of requiring numerous cooperative platforms and the poor accuracy of single-epoch solutions with single-parameter closed-form algorithms, this paper proposes a multi-parameter multi-epoch positioning method based on a three-satellite formation. Simulation data are used to analyze the positioning accuracy under various epochs and different TDOA and AOA noise conditions. The experimental results demonstrate that, compared to the traditional single-parameter single-epoch localization method, utilizing a three-satellite formation with combined AOA and TDOA parameters, along with a multi-epoch solution approach, significantly improves localization accuracy to within an order of ten meters. This method enhances robustness and provides a viable strategy for addressing localization challenges caused by underdetermined systems of equations. Additionally, the results verify that an accumulated almanac element duration of 20 s ensures high positioning accuracy while maintaining a low computational cost. The combined multi-parameter multi-epoch method shows substantial advantages in improving both accuracy and robustness, providing valuable insights for future satellite-based emitter localization technologies. Full article
(This article belongs to the Special Issue Advances in Applications of Remote Sensing GIS and GNSS)
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16 pages, 8837 KiB  
Technical Note
An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring
by Yilin Xie, Song Zhang, Xiaolin Meng, Dinh Tung Nguyen, George Ye and Haiyang Li
Remote Sens. 2024, 16(4), 607; https://doi.org/10.3390/rs16040607 - 6 Feb 2024
Cited by 8 | Viewed by 2819
Abstract
This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively [...] Read more.
This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out. Full article
(This article belongs to the Special Issue Advances in Applications of Remote Sensing GIS and GNSS)
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