A novel custom optimized convolutional neural network for a satellite image by using forest fire detection

Kalaivani, V.; Chanthiya, P.

doi:10.1007/s12145-022-00801-y

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…To evaluate the performance of the WIM, we employed several metrics, Glou, objectness, Classi cation, Precision, Recall, and the mean Average Precision (mAP) (Du, Precision can evaluate the algorithm's positioning and target detection capabilities accurately and reasonably. The Recall is the probability of being predicted to be positive in an actual positive sample (Kalaivani et al,2022). mAP was used to assess the performance of the algorithm, which was appropriate for the multi-label image classi cation task in this study.…”

Section: Resultsmentioning

confidence: 99%

Design and implementation of the intelligent system for automatically monitoring waterbirds in Quanzhou Bar Wetland

Wang

Que

Liu

et al. 2023

Preprint

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The abundance and quantity of waterbird species are often used to evaluate the ecological status of wetlands because most waterbirds are sensitive to the environment. Traditional methods of detecting waterbirds are not only time-consuming but also inaccurate. Some investigations may even be at risk of the natural environment, E.g., bad weather or wild animal attacks. To address this issue, we designed an intelligent waterbird automatic identification system based on Model-View-Viewmodel (MVVM) framework which can support high effectively, safe and long-time monitoring the native wetland waterbirds. To implement the system, we trained a waterbird identification model (WIM) using the YOLOv5 algorithm and deployed it on a back-end for real-time detections, species identifications, and recording counts at different time intervals. It was integrated into a WebGIS-based application, which can be helpful for user to observe the spatial distributions of waterbirds of different species and analyzing their changing laws. We employed a PostgreSQL Database to manage geospatial data and designed the corresponding data access APIs. In addition, a tool for uploading and labeling images online was implemented for pre-training, retraining, and updating the WIM. In our current system, the image Database plays a vital role and it is designed to be auto-update, which means that once our users finished uploading a new image, the pretrained WIM on the back-end will be updated automatically. Although the system is still on its initial testing phase, some results show that it works well. The identifying rate and recall of native waterbird can reach 81.00% and 99.00%, respectively. The ongoing system is able to meet the basic requirements of detecting native wetland waterbird species and record the maximum number of observations of different species within a certain time interval specified by users. And it will provide more information about for managers to better understand the waterbirds and the wetland environment.

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Section: Resultsmentioning

confidence: 99%

Design and implementation of the intelligent system for automatically monitoring waterbirds in Quanzhou Bar Wetland

Wang

Que

Liu

et al. 2023

Preprint

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“…Using 715 forest fire images, MiniVGGNet, ShallowNet, and LeNet achieved an accuracy of 98%, 95%, and 97%, respectively. Kalaivani and Chanthiya [32] proposed a custom optimized CNN which integrated an ALO (Antlion Optimization) method inside a PReLU activation function to detect forest fires. An accuracy of 60.87% was achieved using Landsat satellite images.…”

Section: Systemmentioning

confidence: 99%

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Ghali

Akhloufi

2023

Fire

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Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.

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Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factors

Habeeb,

Mustafa

2024

Earth Sci Inform

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A novel custom optimized convolutional neural network for a satellite image by using forest fire detection

Cited by 6 publications

References 13 publications

Design and implementation of the intelligent system for automatically monitoring waterbirds in Quanzhou Bar Wetland

Design and implementation of the intelligent system for automatically monitoring waterbirds in Quanzhou Bar Wetland

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factors

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