Application of Unmanned Aerial Vehicle (UAV) in the assessment of beach volume change – A case study of Malgund beach

Jeyaraj, Satheeshkumar; Balaji, R.; Ramsankaran, Raaj

doi:10.1109/oceanschennai45887.2022.9775290

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Some researchers have devised solutions based on object recognition, employing cameras affixed to UAVs [6][7][8]. Analyzing moving objects within imagery remains an intriguing field of study, with several solutions designed to identify patterns and extract specific data from images for subsequent analysis [9,10]. UAVs are especially vital in scenarios involving peril, such as natural disasters.…”

Section: Related Workmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Strzępek,

Salach,

Trybus

et al. 2023

Sensors

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This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images.

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Section: Related Workmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Strzępek,

Salach,

Trybus

et al. 2023

Sensors

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“…In this study, the main goal was to compare the accuracy of the data obtained with the use of UAV with the data obtained as a result of TLS. UAVs have already proven themselves for small areas mapping, including coastal zone [Chang et al, 2021;Eisenbeiss, 2011;Gonçalves and Henriques, 2015;Jeyaraj et al, 2022;Remondino et al, 2011;Turner et al, 2016;Zanutta et al, 2020].…”

Section: Introduction 1monitoring Of Changes In the Coastal Zonementioning

confidence: 99%

Comparative Analysis of the Unmanned Aerial Vehicles and Terrestrial Laser Scanning Application for Coastal Zone Monitoring

Danchenkov,

Belov

2023

Russian Journal of Earth Sciences

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The shallow sandy shores of the tideless sea are regularly affected by storm activity. Foredune ridge is a natural and anthropogenic object, a natural protective barrier that protects ecosystems and populated areas from the effects of dangerous hydrometeorological phenomena such as storm surges and wind-sand flux. In the course of impact of dangerous hydrometeorological phenomena, the foredune ridge integrity is disturbed, the composing material is washed away thus forming breakthroughs. Monitoring of the foredune state is an important stage in maintaining its condition and also provides an empirical basis for predicting the impact of hazardous events. The use of ground-based laser scanning technology as well as digital photogrammetry for the study of sensitive coastal zones is justified for these purposes. In this article, we compare the results of calculating the dynamics of the beach sand material and advance them according to the results of ground-based laser scanning and digital photogrammetry. Comparability is provided by high-density clouds of ground-scan points and digital photogrammetry in a single coordinate reference. Two sections of the sensitive coastal zone of the Curonian Spit (Russian sector of the South-Eastern Baltic) have been explored in advance. A comparison of the applicability of means for obtaining digital elevation models to evaluate the dynamics of sand material has been made. In comparison with TLS, the use of UAV with the SfM algorithm is limited to post-storm surveys, since the final accuracy does not provide for reliable lithodynamic studies due to the small scale of processes comparable to measurement errors.

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Application of Unmanned Aerial Vehicle (UAV) in the assessment of beach volume change – A case study of Malgund beach

Cited by 2 publications

References 6 publications

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Comparative Analysis of the Unmanned Aerial Vehicles and Terrestrial Laser Scanning Application for Coastal Zone Monitoring

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