Detection of Wildfires along Transmission Lines Using Deep Time and Space Features

Yuan, Jie; Wang, Lidong; Wu, Peng; Gao, Chao; Sun, Lingqing

doi:10.1134/s1054661818040168

Cited by 9 publications

(10 citation statements)

References 8 publications

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“…Zhang et al 2018;Q.X. Zhang 2018;Yuan et al 2018;Akhloufi et al 2018;Barmpoutis et al 2019;Jakubowski et al 2019;Sousa et al 2019;, T. Li et al 2019Muhammad et al 2018;Wang et al 2019). Of particular note, Q.X.…”

Section: Fire Detectionunclassified

“…found that CNNs outperformed a SVM-based method, and Barmpoutis et al (2019) found that a faster region-based CNN outperformed another CNN based on YOLO ("you only look once"). Yuan et al (2018) used CNN combined with optical flow to include time-dependent information. X. similarly used a three-dimensional CNN to incorporate both spatial and temporal information and so were able to treat smoke detection as a segmentation problem for video images.…”

Section: Fire Detectionmentioning

confidence: 99%

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A review of machine learning applications in wildfire science and management

et al. 2020

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Artiﬁcial intelligence has been applied in wildﬁre science and management since the 1990s, with early applications including neural networks and expert systems. Since then the ﬁeld has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildﬁre science and management. Our overall objective is to improve awareness of ML methods among wildﬁre researchers and managers, as well as illustrate the diverse and challenging range of problems in wildﬁre science available to ML data scientists. To that end, we ﬁrst present an overview of popular ML approaches used in wildﬁre science to date, and then review the use of ML in wildﬁre science as broadly categorized into six problem domains, including: 1) fuels characterization, ﬁre detection, and mapping; 2) ﬁre weather and climate change; 3) ﬁre occurrence, susceptibility, and risk; 4) ﬁre behavior prediction; 5) ﬁre eﬀects; and 6) ﬁre management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildﬁres within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artiﬁcial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildﬁre sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildﬁre science is necessary to ensure realistic modelling of ﬁre processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildﬁre research and management communities play an active role in providing relevant, high quality, and freely available wildﬁre data for use by practitioners of ML methods.

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Section: Fire Detectionunclassified

Section: Fire Detectionmentioning

confidence: 99%

A review of machine learning applications in wildfire science and management

et al. 2020

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“…Before the rise in popularity of deep learning methods, computer vision algorithms leveraging hand-crafted features identified that the visual (e.g., color), spatial, and temporal (i.e., motion) qualities of smoke are essential for the machine detection of wildfires [3,[10][11][12]. More recently, deep learning approaches use a combination of convolutional neural networks (CNNs) [5][6][7][13][14][15][16][17], background subtraction [13,16,18], and object detection methods [4,8,17,19,20] to incorporate visual and spatial features. Long short-term memory (LSTM) networks [4,16] or optical flow [14,18,21] methods have been applied to incorporate temporal context from video sequences.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, deep learning approaches use a combination of convolutional neural networks (CNNs) [5][6][7][13][14][15][16][17], background subtraction [13,16,18], and object detection methods [4,8,17,19,20] to incorporate visual and spatial features. Long short-term memory (LSTM) networks [4,16] or optical flow [14,18,21] methods have been applied to incorporate temporal context from video sequences.…”

Section: Related Workmentioning

confidence: 99%

FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

et al. 2022

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The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response.

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“…Li, Zhao, Zhang, & Hu, 2019;X. Li, Chen, Wu, & Liu, 2018;Muhammad, Ahmad, & Baik, 2018;Wang, Dang, & Ren, 2019;Yuan, Wang, Wu, Gao, & Sun, 2018;B. Zhang, Wei, He, & Guo, 2018;Q.…”

Section: Fire Detectionmentioning

confidence: 99%

A review of machine learning applications in wildfire science and management

Jain,

Coogan,

Subramanian

et al. 2020

Preprint

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Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists.To that end, we first present an overview of popular ML approaches used in wildfire science to date, and then review the use of ML in wildfire science as broadly categorized into six problem domains, including: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, we identified 298 relevant publications, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods -including deep learning and agent based learning -in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high quality, and freely available wildfire data for use by practitioners of ML methods.

show abstract

Detection of Wildfires along Transmission Lines Using Deep Time and Space Features

Cited by 9 publications

References 8 publications

A review of machine learning applications in wildfire science and management

A review of machine learning applications in wildfire science and management

FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

A review of machine learning applications in wildfire science and management

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