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Remote Sensing
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Geospatial Intelligence in Remote Sensing
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Geospatial Intelligence in Remote Sensing

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 June 2025 | Viewed by 1997

Special Issue Editors

Dr. Samsung Lim

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
Interests: natural disaster/emergency management; geospatial epidemiology; data science; artificial intelligence; machine learning; satellite image classification
Special Issues, Collections and Topics in MDPI journals
Dr. Badal Pokharel

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
Interests: remote sensing for geohazard assessment; change detection; predictive modelling; disaster risk reduction and management

Special Issue Information

Dear Colleagues,

Advancements in remote sensing and geospatial intelligence are reshaping the understanding of spatial data. Many researchers have been using machine learning algorithms and sophisticated statistical approaches in remote sensing. In the last few years, innovations in artificial intelligence (AI) in the geospatial field, known as GeoAI or geospatial AI, have significantly enhanced our ability to process and interpret spatial data. Visual interpretation of 2D and 3D remote sensing data plays a vital role in structure failure assessment and monitoring, land use and land cover mapping, geomorphology, and disaster and climate change research, among others. Leveraging these advancements in geospatial intelligence in remote sensing has fostered scientific solutions for tackling challenges in structural monitoring, environmental monitoring, disaster risk reduction and management, urban planning, and climate change adaptation.

This issue aims to compile groundbreaking research that underlines the nexus between geospatial intelligence and remote sensing, aligning with the journal’s focus on advancing our understanding of remote sensing technologies and their applications. This is a platform for researchers to highlight how GeoAI approaches can enhance remote sensing data interpretation and address real-world issues. Therefore, the main objective is to contribute to the journal’s mission of promoting impactful advancements in remote sensing among the scientific community as well as policy practitioners.

We invite scientists, researchers, and practitioners to submit their original articles, reviews, and case studies. Themes revolve around the latest breakthroughs and applications of geospatial intelligence in remote sensing, which include but, are not limited to, the following:

  • Machine learning applications in remote sensing;
  • 3D point cloud processing;
  • Disaster management and climate change adaptation;
  • Environmental monitoring and management;
  • Infrastructure;
  • Urban planning and development;
  • Mining;
  • Agriculture and food security.

Dr. Samsung Lim
Dr. Badal Pokharel
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 intelligence
  • remote sensing
  • machine learning
  • spatial analysis
  • GeoAI
  • 3D point cloud processing
  • environmental monitoring
  • satellite image classification

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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

21 pages, 23238 KiB  
Article
Semantic and Geometric Fusion for Object-Based 3D Change Detection in LiDAR Point Clouds
by Abderrazzaq Kharroubi, Fabio Remondino, Zouhair Ballouch, Rafika Hajji and Roland Billen
Remote Sens. 2025, 17(7), 1311; https://doi.org/10.3390/rs17071311 - 6 Apr 2025
Viewed by 425
Abstract
Accurate three-dimensional change detection is essential for monitoring dynamic environments such as urban areas, infrastructure, and natural landscapes. Point-based methods are sensitive to noise and lack spatial coherence, while object-based approaches rely on clustering, which can miss fine-scale changes. To address these limitations, [...] Read more.
Accurate three-dimensional change detection is essential for monitoring dynamic environments such as urban areas, infrastructure, and natural landscapes. Point-based methods are sensitive to noise and lack spatial coherence, while object-based approaches rely on clustering, which can miss fine-scale changes. To address these limitations, we introduce an object-based change detection framework integrating semantic segmentation and geometric change indicators. The proposed method first classifies bi-temporal point clouds into ground, vegetation, buildings, and moving objects. A cut-pursuit clustering algorithm then segments the data into spatially coherent objects, which are matched across epochs using a nearest-neighbor search based on centroid distance. Changes are characterized by a combination of geometric features—including verticality, sphericity, omnivariance, and surface variation—and semantic information. These features are processed by a random forest classifier to assign change labels. The model is evaluated on the Urb3DCD-v2 dataset, with feature importance analysis to identify important features. Results show an 81.83% mean intersection over union. An additional ablation study without clustering reached 83.43% but was more noise-sensitive, leading to fragmented detections. The proposed method improves the efficiency, interpretability, and spatial coherence of change classification, making it well suited for large-scale monitoring applications. Full article
(This article belongs to the Special Issue Geospatial Intelligence in Remote Sensing)
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21 pages, 21382 KiB  
Article
A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
by Zhibin Li, Yanling Zhao, He Ren and Yueming Sun
Remote Sens. 2024, 16(24), 4648; https://doi.org/10.3390/rs16244648 - 12 Dec 2024
Cited by 1 | Viewed by 1091
Abstract
Open-pit mining offers significant benefits, such as enhanced safety conditions and high efficiency, making it a crucial method for use in the modern coal industry. Nevertheless, the comprehensive process of “stripping-mining-discharge-reclamation” inevitably leads to ecological disturbances in the mine and surrounding areas. Consequently, [...] Read more.
Open-pit mining offers significant benefits, such as enhanced safety conditions and high efficiency, making it a crucial method for use in the modern coal industry. Nevertheless, the comprehensive process of “stripping-mining-discharge-reclamation” inevitably leads to ecological disturbances in the mine and surrounding areas. Consequently, dynamic monitoring and supervision of open-pit mining activities are imperative. Unfortunately, current methods are inadequate for accurately identifying and continuously monitoring bare coal identification using medium spatial resolution satellite images (e.g., Landsat). This is due to the complex environmental conditions around mining areas and the need for specific image acquisition times, which pose significant challenges for large-scale bare coal area mapping. To address these issues, the paper proposes a novel bare coal index (BCI) based on Landsat OLI imagery. This index is derived from the spectral analysis, sensitivity assessment, and separability study of bare coal. The effectiveness and recognition capability of the proposed BCI are rigorously validated. Our findings demonstrate that the BCI can rapidly and accurately identify bare coal, overcoming limitations related to image acquisition timing, thus enabling year-round image availability. Compared to existing identification methods, the BCI exhibits superior resistance to interference in complex environments. The application of the BCI in the Chenqi Coalfield, Shengli Coalfield, and Dongsheng Coalfield in Inner Mongolia, China, yielded an average overall accuracy of 97% and a kappa coefficient of 0.87. Additionally, the BCI was also applied for bare coal area identification across the entire Inner Mongolia region, with a correct classification accuracy of 90.56%. These results confirm that the proposed index is highly effective for bare coal identification and can facilitate digital mapping of extensive bare coal (BC) coverage in open-pit mining areas. Full article
(This article belongs to the Special Issue Geospatial Intelligence in Remote Sensing)
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