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Current Trends Using Cutting-Edge Geospatial Remote Sensing
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Current Trends Using Cutting-Edge Geospatial Remote Sensing

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

Deadline for manuscript submissions: 30 January 2025 | Viewed by 8129

Special Issue Editors

Dr. Min Zhang

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Interests: deep learning; explainable artificial intelligence; remote sensing image processing; change detection; 2D & 3D spatial data quality; web/mobile GIS
Dr. Ming Hao

E-Mail Website
Guest Editor
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: change detection; multisource remote sensing image registration; remote sensing of environment; remote sensing of disasters; deep learning
Dr. Rui Zhu

E-Mail Website
Guest Editor
Systems Science Department, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore 138632, Singapore
Interests: urban heat island; solar cities; urban mobility
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Awrangjeb

E-Mail Website
Guest Editor
Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia
Interests: deep learning; remote sensing image processing; point cloud processing; change detection; object recognition; object modelling; remote sensing data registration; remote sensing of environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The national economy, ecological protection, and national security are all areas that have benefited from the great progress made in geospatial remote sensing technology, including in spectral resolution, spatial resolution, and temporal resolution, and have benefitted from the capabilities of observing the full-time and full weather data of the Earth. On the other hand, many cutting-edge technologies, such as artificial intelligence (AI), edge computing, and the Internet of Things (IoT), have enhanced the dynamic perception, intelligent reasoning, and knowledge discovery capabilities of machine learning algorithms with regard to geographical phenomena and earth science processes. Such technologies provide a large amount of macroscopic and up-to-date spatial data and intelligent methods for monitoring global and regional changes, assisting in the green, healthy, and sustainable development of countries and cities.

Therefore, this Special Issue entitled, “Current Trends Using Cutting-Edge Geospatial Remote Sensing”, calls for manuscripts that demonstrate the current trends and successful applications of geospatial remote sensing in various fields. We welcome: i) papers presenting recent methodological innovations or success stories in applying the latest geospatial remote sensing techniques, and ii) reviews summarizing and delineating cutting-edge geospatial remote sensing techniques in various fields (e.g., agriculture, forestry, hydrology, geography, land use/land cover, natural disaster, and environmental monitoring).

The topics of interest include, but are not limited to:

  • Advanced AI techniques for remote sensing applications;
  • Classification, object recognition, object modelling, time-series analysis, and change detection using geospatial remote sensing;
  • Frontier remote sensing applications in agriculture, forestry, hydrology, oceanology, geography, land use/land cover, natural disaster, environmental monitoring, etc.;
  • Three-dimensional remote sensing and Earth surface modelling;
  • Big data in remote sensing;
  • Remote sensing data processing;
  • Integration of cutting-edge techniques, such as AI, VR/AR reality, Internet of Things (IoT), edge computing, blockchain, etc.;
  • Applications of emerging remote sensing techniques, such as small satellite, stereo satellite, video satellite, UAV, LiDAR, thermal infrared imaging, etc.

Dr. Min Zhang
Dr. Ming Hao
Dr. Rui Zhu
Dr. Mohammad Awrangjeb
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.

Keywords

  • geospatial remote sensing
  • artificial intelligence
  • machine learning
  • deep learning
  • classification and recognition
  • change detection
  • three-dimensional remote sensing
  • remote sensing big data
  • data processing
  • VR/AR reality
  • internet of things (IoT)
  • edge computing
  • blockchain
  • small/stereo/video satellite
  • UAV
  • LiDAR
  • object detection
  • object modelling

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Published Papers (3 papers)

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Research

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27 pages, 3741 KiB  
Article
Dynamic Delay-Sensitive Observation-Data-Processing Task Offloading for Satellite Edge Computing: A Fully-Decentralized Approach
by Ruipeng Zhang, Yanxiang Feng, Yikang Yang, Xiaoling Li and Hengnian Li
Remote Sens. 2024, 16(12), 2184; https://doi.org/10.3390/rs16122184 - 16 Jun 2024
Cited by 1 | Viewed by 722
Abstract
Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, [...] Read more.
Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods. Full article
(This article belongs to the Special Issue Current Trends Using Cutting-Edge Geospatial Remote Sensing)
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23 pages, 12980 KiB  
Article
A Scalable Method to Improve Large-Scale Lidar Topographic Differencing Results
by Minyoung Jung and Jinha Jung
Remote Sens. 2023, 15(17), 4289; https://doi.org/10.3390/rs15174289 - 31 Aug 2023
Cited by 2 | Viewed by 1544
Abstract
Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the [...] Read more.
Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP). Full article
(This article belongs to the Special Issue Current Trends Using Cutting-Edge Geospatial Remote Sensing)
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Review

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35 pages, 8263 KiB  
Review
Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms
by Lukang Wang, Min Zhang, Xu Gao and Wenzhong Shi
Remote Sens. 2024, 16(5), 804; https://doi.org/10.3390/rs16050804 - 25 Feb 2024
Cited by 4 | Viewed by 4396
Abstract
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities [...] Read more.
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field. Full article
(This article belongs to the Special Issue Current Trends Using Cutting-Edge Geospatial Remote Sensing)
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