Wildfire Detection from Sentinel Imagery Using Convolutional Neural Network (CNN)

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dc.contributor.authorAbujayyab, S.K.M.
dc.contributor.authorKaras, I.R.
dc.contributor.authorHashempour, J.
dc.contributor.authorEmircan, E.
dc.contributor.authorOrçun, K.
dc.contributor.authorAhmet, G.
dc.date.accessioned2024-09-29T16:21:16Z
dc.date.available2024-09-29T16:21:16Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description8th International Conference on Smart City Applications, SCA 2023 -- 4 October 2023 through 6 October 2023 -- Paris -- 308359en_US
dc.description.abstractWildfires are a significant threat to the environment and human life, and early detection is crucial for effective wildfire management. The aim of this research work is to develop a deep learning model using CNN method for detecting wildfire using satellite imagery. The CNN model development process involves splitting the dataset into training and testing sets, pre-processing the data using ImageDataGenerator function, building a deep learning model using Sequential function, compiling the model using Adam optimizer, categorical cross-entropy loss function, and accuracy metric, and training the model using the fit generator function. The CNN model architecture includes Conv2D, MaxPooling2D, and Dense layers. The dataset was collected from Sentinel-2 L1C, including 159 fire images and 149 non-fire images from different places in the Mediterranean region of Turkey. The CNN model was developed using the Keras library and trained for 200 epochs using the Adam optimizer. The model achieved an accuracy of 92.5% and a loss of 0.22 on the test set, outperforming existing methods for wildfire detection. The research work contributes to the field of wildfire science and management by providing a deep learning CNN detection model that can accurately predict wildfire behavior using satellite imagery. The outcomes of the research work can enable more effective and efficient wildfire mitigation efforts, improving the safety and well-being of communities affected by wildfires. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.sponsorshipInternational College for Engineering and Management, (IRG-ICEM 2022/2303)en_US
dc.identifier.doi10.1007/978-3-031-54376-0_31
dc.identifier.endpage349en_US
dc.identifier.isbn978-303154375-3
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85189606715en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage341en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-54376-0_31
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9654
dc.identifier.volume938 LNNSen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectSatellite imageryen_US
dc.subjectSentinel-2en_US
dc.subjectWildfire detectionen_US
dc.titleWildfire Detection from Sentinel Imagery Using Convolutional Neural Network (CNN)en_US
dc.typeConference Objecten_US

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