Forest fire detection using spiking neural networks

Luo, Yuling; Lü, Qian; Liu, Junxiu; Fu, Qiang; Harkin, Jim; McDaid, Liam; Martínez-Corral, Jordi; Biot-Marí, Guillermo

doi:10.1145/3203217.3203231

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Researchers have studied different approaches to building forest fire detection models. Among these, a forest fire detection method employing spiking neural networks stands out, as detailed in [8]. This approach harnesses data derived from controlled experiments, utilizing it as training samples to forge a detection model.…”

Section: Forest Fire Detectionmentioning

confidence: 99%

Advancing Forest Fire Risk Evaluation: An Integrated Framework for Visualizing Area-Specific Forest Fire Risks Using UAV Imagery, Object Detection and Color Mapping Techniques

Aibin,

Li,

Sharma

et al. 2024

Drones

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Forest fires have significant implications for the Earth’s ecological balance, causing widespread devastation and posing formidable challenges for containment once they propagate. The development of computer vision methods holds promise in facilitating the timely identification of forest fire risks, thereby preventing potential economic losses. In our study conducted in various regions in British Columbia, we utilized image data captured by unmanned aerial vehicles (UAVs) and computer vision methods to detect various types of trees, including alive trees, debris (logs on the ground), beetle- and fire-impacted trees, and dead trees that pose a risk of a forest fire. We then designed and implemented a novel sliding window technique to process large forest areas as georeferenced orthogonal maps. The model demonstrates proficiency in identifying various tree types, excelling in detecting healthy trees with precision and recall scores of 0.904 and 0.848, respectively. Its effectiveness in recognizing trees killed by beetles is somewhat limited, likely due to the smaller number of examples available in the dataset. After the tree types are detected, we generate color maps, indicating different fire risks to provide a new tool for fire managers to assess and implement prevention strategies. This study stands out for its integration of UAV technology and computer vision in forest fire risk assessment, marking a significant step forward in ecological protection and sustainable forest management.

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Section: Forest Fire Detectionmentioning

confidence: 99%

Advancing Forest Fire Risk Evaluation: An Integrated Framework for Visualizing Area-Specific Forest Fire Risks Using UAV Imagery, Object Detection and Color Mapping Techniques

Aibin,

Li,

Sharma

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…A Support Vector Machine (SVM) and Linear Regression (LR) were used in Wang et al ( 2019 ), but recognition accuracy can be improved. In recent years, deep neural networks (DNN) (Tripathi et al, 2017 ) has been developed into one of the most effective and popular methods in many research fields (Fu et al, 2017 ; Liu et al, 2018a , b , 2019 ; Luo et al, 2018 ). Convolutional Neural Networks (CNN) are widely used in computer vision, image classifications, visual tracking (Danelljan et al, 2016 ), segmentation, and object detections (Girshick et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Liu

Luo

et al. 2020

Front. Syst. Neurosci.

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179

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Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN.

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Automatic Segmentation and Diagnosis of Intervertebral Discs Based on Deep Neural Networks

Liang

Liu

Luo

et al. 2020

Communications in Computer and Information Science

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Forest fire detection using spiking neural networks

Cited by 8 publications

References 15 publications

Advancing Forest Fire Risk Evaluation: An Integrated Framework for Visualizing Area-Specific Forest Fire Risks Using UAV Imagery, Object Detection and Color Mapping Techniques

Advancing Forest Fire Risk Evaluation: An Integrated Framework for Visualizing Area-Specific Forest Fire Risks Using UAV Imagery, Object Detection and Color Mapping Techniques

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Automatic Segmentation and Diagnosis of Intervertebral Discs Based on Deep Neural Networks

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