Use of satellite images for forest fires in area determination and monitoring

Güre, Mülayim; Özel, M. E.; Yıldırım, H.; Özdemir, Muzaffer

doi:10.1109/rast.2009.5158210

Cited by 11 publications

(5 citation statements)

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“…𝑡 ̂ is the target neuron in the channel, 𝑥 is the other neurons in the channel, i represents the index denoting the spatial dimension, and M signifies the count of neurons present within that specific channel. By binary labeling and adding the canonical term, the ultimate formulation of the energy function is depicted as presented in Equation (5).…”

Section: 𝑒 (𝑤 𝑏 𝑦 𝑥 ) = (𝑦 − 𝑡mentioning

confidence: 99%

“…However, the cost of patrolling is high and cannot be monitored continuously on a large scale for a long time [4]. Satellite early warning monitoring monitors the heat sources of objects on the ground through artificial satellites, but it will be disturbed by factors such as high temperature, bare ground, and strong reflectors, resulting in misjudgment and omission of fire points [5].…”

Section: Introductionmentioning

confidence: 99%

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A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

Qian,

Bai,

Jiao

et al. 2023

Forests

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Forest fires are major forestry disasters that cause loss of forest resources, forest ecosystem safety, and personal injury. It is often difficult for current forest fire detection models to achieve high detection accuracy on both large and small targets at the same time. In addition, most of the existing forest fire detection models are single detection models, and using only a single model for fire detection in a complex forest environment has a high misclassification rate, and the accuracy rate needs to be improved. Aiming at the above problems, this paper designs two forest fire detection models (named WSB and WSS) and proposes an integrated learning-based forest fire detection model (named WSB_WSS), which also obtains high accuracy in the detection of forest fires with large and small targets. In order to help the model predict the location and size of forest fire targets more accurately, a new edge loss function, Wise-Faster Intersection over Union (WFIoU), is designed in this paper, which effectively improves the performance of the forest fire detection algorithm. The WSB model introduces the Simple-Attention-Module (SimAM) attention mechanism to make the image feature extraction more accurate and introduces the bi-directional connectivity and cross-layer feature fusion to enhance the information mobility and feature expression ability of the feature pyramid network. The WSS model introduces the Squeeze-and-Excitation Networks (SE) attention mechanism so that the model can pay more attention to the most informative forest fire features and suppress unimportant features, and proposes Spatial Pyramid Pooling-Fast Cross Stage Partial Networks (SPPFCSPC) to enable the network to extract features better and speed up the operation of the model. The experimental findings demonstrate that the WSB model outperforms other approaches in the context of identifying forest fires characterized by small-scale targets, achieving a commendable accuracy rate of 82.4%, while the WSS model obtains a higher accuracy of 92.8% in the identification of large target forest fires. Therefore, in this paper, a more efficient forest fire detection model, WSB_WSS, is proposed by integrating the two models through the method of Weighted Boxes Fusion (WBF), and the accuracy of detecting forest fires characterized by small-scale targets attains 83.3%, while for forest fires with larger dimensions, the accuracy reaches an impressive 93.5%. This outcome effectively leverages the strengths inherent in both models, consequently achieving the dual objective of high-precision detection for both small and large target forest fires concurrently.

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Section: 𝑒 (𝑤 𝑏 𝑦 𝑥 ) = (𝑦 − 𝑡mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

Qian,

Bai,

Jiao

et al. 2023

Forests

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“…The proposed method achieved high accuracy in detecting forest fires. Kavak, S. [2] proposed a forest fire detection system based on deep learning techniques, specifically using a deep convolutional neural network (DCNN). The model demonstrated promising results in accurately identifying fire regions.…”

Section: IImentioning

confidence: 99%

Forest Fire Detection using Deep Learning

Sreekanth,

Sriram,

Sujan

2023

IJRASET

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The threat posed by forest fires to humanity has increased. In addition to giving a lot of living things a place to live and protection, they have also been a significant source of food, wood, and a lot of other goods. Throughout the dawn of time, woods have been integral to social, economic, and religious endeavors and have improved human life in numerous tangible and intangible ways. We must exercise sufficient caution when making decisions that could ultimately result in a tragic outcome if we want to safeguard our environment from these forest fires that are spreading quickly. Thus, we suggest an image identification technique based on convolutional neural networks for the early detection of forest fires (CNN).

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“…A study conducted by [18] shows the classification of the objects according to the development of proceedings of classification for the burning area map and some fire damages that have happened from the summer of (2007) to the summer of (2009) in Greece, via the presentation of the pour system" observation de la Terre (SPOT4) HRVIR images". In [19][20] [21], have presented a classification of the satellite imagery environments using a creative approach that has been referred to as the RBPM (i.e. the Reflection Based Phenology Method), in which Landsat 8 datasets were utilized, keeping the image in the multi-spectral.…”

Section: Figure 1-the Location Of Area Study In Australiamentioning

confidence: 99%

Classification and Measurement of Land Cover of Wildfires in Australia Using Remote Sensing

Hassan

Saeed

Al-Ghrairi

2022

eijs

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Remote sensing techniques used in many studies for classfying and measuring of wildfires. Satellite Landsat8(OLI) imagery is used in the presented work. The satellite is considered as a near-polar orbit, with a high multispectral resolution for covering Wollemi National Park in Australia. The work aims to study and measure wildfire natural resources prior to and throughout fire breakout which occurred in Wollemi National Park in Australia for a year (October, 2019), as well as analyzing the harm resulting from such wildfires and their effects on earth and environment through recognizing satellite images for studied region prior to and throughout wildfires. A discussion of methods for computing the affecred area is covered regarding each one of the classes and lessening or limiting the quickly-spreading wildfires damage. This paper propose a 2-phases techniques: training and classifying. In the training phase, the number of clustering is computed by using C# Programming Language and feature extracted and clustered as a group and stored in the dataset. The classification used the moments with (K-Means) classification approach in RS (Remote Sensing) for classified image. The results of classification showed 5 distinctive classes (trees, rivers, bare earth, buildings with no trees, and buildings with trees) in which it might be indicates that the region is secured via each one of the classes prior to and throughout wildfires as well as the changed pixels with regard to all the classes. Also, the classification experimental methods results indicate an excellent performance recision with a good classifying and result analysis about the harms caused by fires in the study area.

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Use of satellite images for forest fires in area determination and monitoring

Cited by 11 publications

References 0 publications

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

Forest Fire Detection using Deep Learning

Classification and Measurement of Land Cover of Wildfires in Australia Using Remote Sensing

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