Smoke detection in a digital image with the use of convolutional network

Jakubowski, Jacek; Solarczyk, Maciej Tomasz; Wiśnios, M.

doi:10.1117/12.2524560

Cited by 3 publications

(3 citation statements)

References 9 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

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

A review of machine learning applications in wildfire science and management

et al. 2020

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“…Some of these applications are trained on terrestrial image-based models of fire and/or smoke. Several studies can be found in this field [33][34][35] .…”

Section: Forest and Land Fire Detectionmentioning

confidence: 99%

Machine Learning Application in Response to Disaster Risk Reduction of Forest and Peatland Fire

Riza¹,

Santoso

Kristijono

et al. 2023

Majalah Ilmiah Pengkajian Industri

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Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that have not been decomposed. In the dry season, this accumulated biomass called peat, which was originally submerged in water, is exposed to the surface and prone to fire. Once it burns, smouldering peat fires consume biomass 15 times larger than open flame. Peat smouldering fires are very difficult to extinguish. These will continuously occur for weeks to months. The most recommended effort by experts and practitioners of peat smouldering fires is to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology'. Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses (1) the accuracy is very low; (2) having predictive biases in areas with different ecosystem characteristics; (3) it is often difficult to implement because it requires a large and complex number of 'expert rules'; and (4) involving many variables, complex, and heterogeneous data formats. Slowly but surely this method has been replaced by the Machine Learning (ML) method as it is developing in Europe, China, India, Japan, North America and Australia. What about the potential application of ML in the forest and land fires, especially smouldering peat fires in Indonesia? This paper tries to answer this question through the following points: State of the art of machine learning in science and management of forest and land fire Outlook on technology of Disaster Risk Reduction (DRR) in BPPT in the field of forest and land fire and the opportunities of ML implementation Impact based forecasting and machine learning for DRR of forest and land fire This paper recommends a conceptual design: Impact-Based Learning for DRR of Forest, Land Fire and Peat Smouldering

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“…deep learning), which are able to extract features and patterns from spatial images and are finding widespread use in object detection tasks, have recently been applied to the problem of fire detection. Several of these applications trained the models on terrestrial based images of fire and/or smoke (Akhloufi, Booto Tokime, & Elassady, 2018;Barmpoutis, Dimitropoulos, Kaza, & Grammalidis, 2019;Jakubowski, Solarczyk, & Wiśnios, 2019;João Sousa, Moutinho, & Almeida, 2019;T. Li, Zhao, Zhang, & Hu, 2019;X.…”

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.

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Smoke detection in a digital image with the use of convolutional network

Cited by 3 publications

References 9 publications

A review of machine learning applications in wildfire science and management

A review of machine learning applications in wildfire science and management

Machine Learning Application in Response to Disaster Risk Reduction of Forest and Peatland Fire

A review of machine learning applications in wildfire science and management

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