Abstract
Wildfires pose a major risk to humans and other species, but thanks to advances in remote sensing techniques, they are now being continuously observed and regulated. The existence of wildfires in the environment is indicated by the deposition of smoke in the atmosphere. Observation of fire is critical in fire alarm systems for reducing losses and other fire hazards with social consequences. To avoid massive fires, effective detectors from visual scenarios are crucial. A convolution neural network (CNN)-based system has been used to improve fire detection accuracy. Segregating inputs into training and testing subspace is a vital aspect of the Inception—v3 architecture (Vani in Deep learning based forest fire classification and detection in satellite images. IEEE, pp 61–65, 2019 [6]). By default, a maximal and minimal amount of data are used for training and testing, and accuracy vs loss graphs for training and testing data are plotted for data visualization.
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I, the corresponding author, declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.
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NISHANTH P
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Nishanth, P., Varaprasad, G. (2023). Forest Fire Detection and Classification Using Deep Learning Concepts. In: Sharma, N., Goje, A., Chakrabarti, A., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2023. Lecture Notes in Networks and Systems, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-99-1414-2_3
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DOI: https://doi.org/10.1007/978-981-99-1414-2_3
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