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Coastal Environment Monitoring Based on Remote Sensing and Artificial Intelligence

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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

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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 881

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)

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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 2 | Viewed by 3073

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)

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