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Remote Sensing
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Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence
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Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 6364

Special Issue Editors

Prof. Dr. Scot Smith

E-Mail Website
Guest Editor
School of Forest Resources & Conservation, University of Florida, Gainesville, FL 32611, USA
Interests: remote sensing; GIS; photogrammetry; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Kamal Darwish

E-Mail Website
Guest Editor
Geography Department, Minia University, Al Minia 61519, Egypt
Interests: GIS; AI; geospatial technologies; coastal remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence is a critical scientific topic. Coastlines worldwide are threatened by increases in the sea level, as well as other man-made and natural processes. The advent of modern high-resolution satellite imagery and artificial intelligence aims to improve coastal zone monitoring and management.

This Special Issue will provide a forum for discussion on the latest means of monitoring coasts using remote sensing and artificial intelligence. Potential topics include (but are not limited to): Satellite imagery for coastal mapping, LiDAR for coastal mapping, artificial intelligence for coastal mapping, GoogleEarth and coastal mapping, sea-level rise measurements obtained via remote sensing, drones and coastal mapping, coral reef mapping, and coastal erosion assessment.

Prof. Dr. Scot Smith
Dr. Kamal Darwish
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

  • artificial intelligence
  • remote sensing
  • coastal monitoring
  • LiDAR
  • satellite imagery

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Published Papers (3 papers)

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Research

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33 pages, 4254 KiB  
Article
A Method of Simplified Synthetic Objects Creation for Detection of Underwater Objects from Remote Sensing Data Using YOLO Networks
by Daniel Klukowski, Jacek Lubczonek and Pawel Adamski
Remote Sens. 2025, 17(15), 2707; https://doi.org/10.3390/rs17152707 - 5 Aug 2025
Viewed by 403
Abstract
The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). The research was conducted on inland shallow water [...] Read more.
The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). The research was conducted on inland shallow water areas, while images of bottom objects were obtained using a UAV platform. The work consisted in preparing SSOs, thanks to which composite images were created. On such training data, 120 models based on the YOLO (You Only Look Once) network were obtained. The study confirmed the effectiveness of models created using YOLOv3, YOLOv5, YOLOv8, YOLOv9, and YOLOv10. A comparison was made between versions of YOLO. The influence of the amount of training data, SSO type, and augmentation parameters used in the training process was analyzed. The main parameter of model performance was the F1-score. The calculated statistics of individual models indicate that the most effective networks use partial augmentation, trained on sets consisting of 2000 SSOs. On the other hand, the increased transparency of SSOs resulted in increasing the diversity of training data and improving the performance of models. This research is developmental, and further research should improve the processes of obtaining detection models using deep networks. Full article
(This article belongs to the Special Issue Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence)
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20 pages, 4607 KiB  
Article
Deep Learning-Based Real-Time Surf Detection Model During Typhoon Events
by Yucheng Shi, Guangjun Xu, Yuli Liu, Hongxia Chen, Shuyi Zhou, Jinxiang Yang, Changming Dong, Zhixia Lin and Jialun Wu
Remote Sens. 2025, 17(6), 1039; https://doi.org/10.3390/rs17061039 - 16 Mar 2025
Viewed by 959
Abstract
Surf during typhoon events poses severe threats to coastal infrastructure and public safety. Traditional monitoring approaches, including in situ sensors and numerical simulations, face inherent limitations in capturing surf impacts—sensors are constrained by point-based measurements, while simulations require intensive computational resources for real-time [...] Read more.
Surf during typhoon events poses severe threats to coastal infrastructure and public safety. Traditional monitoring approaches, including in situ sensors and numerical simulations, face inherent limitations in capturing surf impacts—sensors are constrained by point-based measurements, while simulations require intensive computational resources for real-time monitoring. Video-based monitoring offers promising potential for continuous surf observation, yet the development of deep learning models for surf detection remains underexplored, primarily due to the lack of high-quality training datasets from typhoon events. To bridge this gap, we propose a lightweight YOLO (You Only Look Once) based framework for real-time surf detection. A novel dataset of 2855 labeled images with surf annotations, collected from five typhoon events at the Chongwu Tide Gauge Station, captures diverse scenarios such as daytime, nighttime, and extreme weather conditions. The proposed YOLOv6n model achieved 99.3% mAP50 at 161.8 FPS, outperforming both other YOLO variants and traditional two-stage detectors in accuracy and computational efficiency. Scaling analysis further revealed that YOLO models with 2–5 M parameters provide an optimal trade-off between accuracy and computational efficiency. These findings demonstrate the effectiveness of YOLO-based video monitoring systems for real-time surf detection, offering a practical and reliable solution for coastal hazard monitoring under extreme weather conditions. Full article
(This article belongs to the Special Issue Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence)
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33 pages, 50318 KiB  
Technical Note
A New Open-Source Software to Help Design Models for Automatic 3D Point Cloud Classification in Coastal Studies
by Xavier Pellerin Le Bas, Laurent Froideval, Adan Mouko, Christophe Conessa, Laurent Benoit and Laurent Perez
Remote Sens. 2024, 16(16), 2891; https://doi.org/10.3390/rs16162891 - 8 Aug 2024
Cited by 2 | Viewed by 3390
Abstract
This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. [...] Read more.
This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. Input data can be imported via CSV or LAS files, providing a 3D point cloud, enhanced with geometric features or spectral information, such as colors from orthophotos or hyperspectral data. cLASpy_T lets the user run three supervised machine learning algorithms from the scikit-learn API to build automatic classification models: RandomForestClassifier, GradientBoostingClassifier and MLPClassifier. This work presents the general method for classification model design using cLASpy_T and the software’s complete workflow with an example of photogrammetry point cloud classification. Four photogrammetric models of a coastal dike were acquired on four different dates, in 2021. The aim is to classify each point according to whether it belongs to the ‘sand’ class of the beach, the ‘rock’ class of the riprap, or the ‘block’ class of the concrete blocks. This case study highlights the importance of adjusting algorithm parameters, selecting features, and the large number of tests necessary to design a classification model that can be generalized and used in production. Full article
(This article belongs to the Special Issue Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence)
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