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Remote Sensing
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Advanced AI Technology in Remote Sensing
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Advanced AI Technology in Remote Sensing

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

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 2579

Special Issue Editor

Dr. Jing Zhang

E-Mail Website
Guest Editor
Department of Information Engineering and Computer Science at University of Trento, 38123 Trento, Italy
Interests: sematic change detection; remote sensing image processing; semantic segmentation

Special Issue Information

Dear Colleagues,

Remote sensing, the process of gathering data about Earth's surface from a distance, has long been pivotal in environmental monitoring, disaster management, and resource mapping. However, the exponential growth in data volume and complexity has necessitated more sophisticated analysis techniques. Advanced AI technology has emerged as a game-changer, revolutionizing remote sensing by automating data analysis and unlocking deeper insights from vast datasets. Through machine learning algorithms and neural networks, AI can swiftly interpret remote sensing imagery, detect patterns, and predict environmental changes with unparalleled accuracy. This synergy between AI and remote sensing not only enhances efficiency but also empowers decision-makers with actionable information for addressing pressing global challenges, from climate change mitigation to sustainable land management.

Remote sensing, the process of gathering data about Earth's surface from a distance, has long been pivotal in environmental monitoring, disaster management, and resource mapping. However, the exponential growth in data volume and complexity has necessitated more sophisticated analysis techniques. Advanced AI technology has emerged as a game-changer, revolutionizing remote sensing by automating data analysis and unlocking deeper insights from vast datasets. Through machine learning algorithms and neural networks, AI can swiftly interpret remote sensing imagery, detect patterns, and predict environmental changes with unparalleled accuracy. This synergy between AI and remote sensing not only enhances efficiency but also empowers decision-makers with actionable information for addressing pressing global challenges, from climate change mitigation to sustainable land management.

The Special Issue is focused on automated feature detection, classification, change detection, semantic change detection, earth observation, and Lidar and laser scanning. Articles may address, but are not limited, to the following topics:

  • AI-based feature detection and classification;
  • Change detection and monitoring;
  • Predictive modeling and environmental forecasting;
  • Fusion of multi-source remote sensing data;
  • AI for remote sensing data analysis in developing regions;
  • Smart cities and urban innovation.

Dr. Jing Zhang
Guest Editor

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.

Keywords

  • semantic segmentation
  • change detection
  • semantic change detection
  • earth observation
  • lidar and laser scanning
  • remote sensing applications

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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

27 pages, 6723 KiB  
Article
RS-FeatFuseNet: An Integrated Remote Sensing Object Detection Model with Enhanced Feature Extraction
by Yijuan Qiu, Jiefeng Xue, Gang Zhang, Xuying Hao, Tao Lei and Ping Jiang
Remote Sens. 2025, 17(1), 61; https://doi.org/10.3390/rs17010061 - 27 Dec 2024
Cited by 1 | Viewed by 847
Abstract
With the advancement of satellite and sensor technologies, remote sensing images are playing crucial roles in both civilian and military domains. This paper addresses challenges such as complex backgrounds and scale variations in remote sensing images by proposing a novel attention mechanism called [...] Read more.
With the advancement of satellite and sensor technologies, remote sensing images are playing crucial roles in both civilian and military domains. This paper addresses challenges such as complex backgrounds and scale variations in remote sensing images by proposing a novel attention mechanism called ESHA. This mechanism effectively integrates multi-scale feature information and introduces a multi-head self-attention (MHSA) to better capture contextual information surrounding objects, enhancing the model’s ability to perceive complex scenes. Additionally, we optimized the C2f module of YOLOv8, which enhances the model’s representational capacity by introducing a parallel multi-branch structure to learn features at different levels, resolving feature scarcity issues. During training, we utilized focal loss to handle the issue of imbalanced target class distributions in remote sensing datasets, improving the detection accuracy of challenging objects. The final network model achieved training accuracies of 89.1%, 91.6%, and 73.2% on the DIOR, NWPU VHR-10, and VEDAI datasets, respectively. Full article
(This article belongs to the Special Issue Advanced AI Technology in Remote Sensing)
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25 pages, 17064 KiB  
Article
An Environment Recognition Algorithm for Staircase Climbing Robots
by Yanjie Liu, Yanlong Wei, Chao Wang and Heng Wu
Remote Sens. 2024, 16(24), 4718; https://doi.org/10.3390/rs16244718 - 17 Dec 2024
Viewed by 1067
Abstract
For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step [...] Read more.
For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios. Full article
(This article belongs to the Special Issue Advanced AI Technology in Remote Sensing)
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