The application of UAV images in flood detection using image segmentation techniques

Ibrahim, Nur; Sharun, S. M.; Osman, Muhammad Khusairi; Mohamed, Sherihan; Abdullah, Sharifah Hanis Yasmin Sayid

doi:10.11591/ijeecs.v23.i2.pp1219-1226

Cited by 13 publications

(14 citation statements)

References 19 publications

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“…Additionally, GPS aids in pinpointing affected areas and guiding people to safer locations during oods. (Ibrahim et al, 2021) mentioned in their study that researchers are increasingly intrigued by utilizing unmanned aerial vehicles (UAVs) to capture ood area images due to their cost-effectiveness and realtime capabilities. Currently, manual analysis of these images by human experts is laborious, timeconsuming, and error-prone.…”

Section: Previous Studiesmentioning

confidence: 99%

Flood Detection System: Text-Driven Flood Alert Systems with IoT Arduino

Singh,

Dey,

Pandey

2023

Preprint

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Flood detection is a critical aspect of disaster management, aiming to provide timely alerts and mitigate potential damage. This research presents a novel approach to flood detection by integrating IoT Arduino technology with text-driven flood alert systems. The primary objective of this study is to design and develop a cost-effective and efficient flood detection system that leverages Internet of Things (IoT) capabilities to deliver real-time flood alerts through text messages. The methodology employed in this research involves the deployment of Arduino-based sensors in flood-prone areas to monitor water levels. These sensors continuously collect data and transmit it to a central processing unit, which analyses the data for flood patterns. When a potential flood is detected, the system sends instant text messages to local authorities and residents, enabling rapid response and evacuation if necessary. Additionally, the system allows users to customize alert thresholds and receive alerts based on their preferences, enhancing its user-friendliness. The contributions of this research are two-fold. Firstly, it introduces an innovative flood detection system that is not only affordable but also highly accessible to communities in flood-prone regions. By utilizing text messages, it ensures that alerts reach a wide audience, including those without access to smartphones or the internet. Secondly, the system's flexibility and customization options empower users to tailor alerts to their specific needs, promoting community engagement and preparedness. In conclusion, this research offers a robust flood detection solution that combines IoT technology with user-friendly text-driven alerts, ultimately enhancing disaster resilience in vulnerable regions.

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Section: Previous Studiesmentioning

confidence: 99%

Flood Detection System: Text-Driven Flood Alert Systems with IoT Arduino

Singh,

Dey,

Pandey

2023

Preprint

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“…The authors of [21] propose a method for detecting floods using images of Red-Green-Blue (RGB) and Hue, Saturation, Intensity (HIS) color models from UAVs. A k-means segmentation method and a region augmentation method are proposed to highlight flooded and non-flooded areas in the image.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…A k-means segmentation method and a region augmentation method are proposed to highlight flooded and non-flooded areas in the image. The advantage of [21] is a good result of image segmentation. The disadvantage of [21] is the separation of the image into only two segments.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm

Khudov

Makoveichuk

Khudov

et al. 2022

EEJET

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The object of this study is the process of segmentation of images acquired from space optoelectronic surveillance systems. The method to segment images from space optoelectronic surveillance systems based on the Sine-Cosine algorithm involves determining the threshold level; unlike the known ones, the following is carried out in it: – preliminary selection of red-green-blue color space brightness channels in the original image; – calculation of the maximum distance of movement of agents in the image in each brightness channel; – calculation of the values that determine the movement of agents in the image in each brightness channel; – determining the position of agents in the image using trigonometric functions of the sine and cosine in each brightness channel. An experimental study into segmenting images acquired from space optoelectronic surveillance systems based on the Sine-Cosine algorithm was carried out. It was found that the improved method of image segmentation based on the Sine-Cosine algorithm makes it possible to segment the images. In this case, objects of interest, snow-covered objects of interest, background objects, and undefined areas of the image (anomalous areas) are identified. The quality of image segmentation was assessed using the Sine-Cosine algorithm-based method. It was found that the improved segmentation method based on the Sine-Cosine algorithm reduces the segmentation error of the first kind by an average of 21 % and the segmentation error of the first kind by an average of 17 %. Methods of image segmentation can be implemented in software and hardware systems that process images acquired from space optoelectronic surveillance systems. Further studies may involve comparing the quality of segmentation by the method based on the Sine-Cosine algorithm with segmentation methods based on evolutionary algorithms (for example, genetic ones).

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“…Image segmentation tasks have witnessed remarkable achievements thanks to the advancements in deep learning-based approaches, specifically convolutional neural networks (CNNs). Several architectures, such as fully convolutional neural network (FCN) [8], U-Net [9], DeconvNet [10], and SegNet [11], have been proposed and exhibited impressive performance across diverse semantic segmentation tasks [12]- [17]. Based on the success of these architectures, various methods have been proposed to improve performance of road and vehicle segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery

Nguyen,

Pham,

et al. 2024

Bulletin EEI

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Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.

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The application of UAV images in flood detection using image segmentation techniques

Cited by 13 publications

References 19 publications

Flood Detection System: Text-Driven Flood Alert Systems with IoT Arduino

Flood Detection System: Text-Driven Flood Alert Systems with IoT Arduino

Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery

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