Remote Sensing

Journal Browser

► Journal Browser

Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence

Share This Special Issue

Special Issue Editors

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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.

Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.

Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.

External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.

Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

20 pages, 4607 KiB

Open AccessArticle

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 734

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 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%

m A P_{50}

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)

► Show Figures

Figure 1

Other

Jump to: Research

33 pages, 50318 KiB

Open AccessTechnical 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 1 | Viewed by 2845

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. 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)

► Show Figures

Journal Menu

Journal Browser

Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (2 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI