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Artificial Intelligence in Remote Sensing: Advancing Geospatial Analysis for Environmental Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1665

Special Issue Editors

Dr. Sungkwan Youm

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Wonkwang University, Iksan 54538, Republic of Korea
Interests: large language models; intrusion detection; wifi sensing; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Eui-Jik Kim

E-Mail Website
Guest Editor
Division of Software, Hallym University, 1 Hallymdaehak-gil, Chuncheon 24252, Gangwon-do, Republic of Korea
Interests: Internet of Things; cyber physical systems; industrial internet; Industry 4.0; energy harvesting; wireless powered communication networks; artificial intelligence; deep learning; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Taeyoon Kim

E-Mail
Guest Editor
Department of Mobile System Engineering, Dankook University, Yongin-si 16890, Gyeonggi-do, Republic of Korea
Interests: software defined networking; wireless communications systems; wireless communications and networks; network 5G; vehicular communications

Special Issue Information

Dear Colleagues,

Recent advancements in artificial intelligence have opened new frontiers in remote sensing and geospatial analysis, particularly in environmental monitoring. This Special Issue will bring together researchers, practitioners, and innovators to present their latest findings, methodologies, and applications that leverage AI to enhance our understanding and management of the environment through remote sensing technologies.

This Special Issue aims to explore and showcase cutting-edge applications of artificial intelligence (AI) in remote sensing and geospatial analysis for environmental monitoring. We seek to highlight how AI technologies are revolutionizing our ability to collect, process, analyze, and interpret large-scale environmental data from various remote sensing platforms.

The Special Issue will cover a wide range of topics, including, but not limited to, the following:

  1. Machine learning and deep learning for satellite image analysis;
  2. AI-driven change detection in time-series remote sensing data;
  3. Multi-sensor data fusion using AI techniques;
  4. AI applications in hyperspectral image analysis;
  5. Object detection and tracking in remote sensing imagery;
  6. AI-powered predictive modeling for environmental phenomena;
  7. Natural language processing for geospatial metadata analysis.

Dr. Sungkwan Youm
Dr. Eui-Jik Kim
Dr. Taeyoon Kim
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. Applied Sciences 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 2400 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

  • artificial intelligence
  • machine learning
  • deep learning
  • remote sensing
  • geospatial analysis
  • environmental monitoring
  • satellite imagery
  • hyperspectral imaging
  • LiDAR
  • change detection
  • image classification

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

Published Papers (3 papers)

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Research

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18 pages, 16528 KiB  
Article
Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy
by Marica Rondinone, Silvano Fortunato Dal Sasso, Htay Htay Aung, Lucia Contillo, Giusy Dimola, Marcello Schiattarella, Mauro Fiorentino and Vito Telesca
Appl. Sci. 2025, 15(10), 5290; https://doi.org/10.3390/app15105290 - 9 May 2025
Abstract
Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. [...] Read more.
Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide. Full article
(This article belongs to the Special Issue Artificial Intelligence in Remote Sensing: Advancing Geospatial Analysis for Environmental Monitoring)
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20 pages, 3279 KiB  
Article
Slot Occupancy-Based Collision Avoidance Algorithm for Very-High-Frequency Data Exchange System Network in Maritime Internet of Things
by Sol-Bee Lee, Jung-Hyok Kwon, Bu-Young Kim, Woo-Seong Shim, Taeshik Shon and Eui-Jik Kim
Appl. Sci. 2024, 14(24), 11751; https://doi.org/10.3390/app142411751 - 16 Dec 2024
Viewed by 965
Abstract
The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the [...] Read more.
The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence in Remote Sensing: Advancing Geospatial Analysis for Environmental Monitoring)
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Other

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16 pages, 1389 KiB  
Technical Note
Evaluation of Cloud Mask Performance of KOMPSAT-3 Top-of-Atmosphere Reflectance Incorporating Deeplabv3+ with Resnet 101 Model
by Suhwan Kim, Doehee Han, Yejin Lee, Eunsu Doo, Han Oh, Jonghan Ko and Jongmin Yeom
Appl. Sci. 2025, 15(8), 4339; https://doi.org/10.3390/app15084339 - 14 Apr 2025
Viewed by 234
Abstract
Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model [...] Read more.
Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model with a ResNet-101 backbone. To overcome the limitations of digital number (DN) data, Top-of-Atmosphere (TOA) reflectance was computed and used for model training. Comparative analysis between the DN and TOA reflectance demonstrated significant improvements with the TOA correction applied. The TOA reflectance combined with the NDVI channel achieved the highest precision (69.33%) and F1-score (59.27%), along with a mean Intersection over Union (mIoU) of 46.5%, outperforming all the other configurations. In particular, this combination was highly effective in detecting dense clouds, achieving an mIoU of 48.12%, while the Near-Infrared, green, and red (NGR) combination performed best in identifying cloud shadows with an mIoU of 23.32%. These findings highlight the critical role of radiometric correction and optimal channel selection in enhancing deep learning-based cloud detection. This study demonstrates the crucial role of radiometric correction, optimal channel selection, and the integration of additional synthetic indices in enhancing deep learning-based cloud detection performance, providing a foundation for the development of more refined cloud masking techniques in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence in Remote Sensing: Advancing Geospatial Analysis for Environmental Monitoring)
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