Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring

Wildfires have significant ecological, social, and economic impacts, release large amounts of pollutants, and pose a threat to human health. Although deep learning models outperform traditional methods in predicting wildfires, their accuracy drops to about 90% when using remotely sensed data. To eff...

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Main Authors: Shiying Yu, Minerva Singh
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Fire
Subjects:
Online Access:https://www.mdpi.com/2571-6255/8/1/19
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author Shiying Yu
Minerva Singh
author_facet Shiying Yu
Minerva Singh
author_sort Shiying Yu
collection DOAJ
description Wildfires have significant ecological, social, and economic impacts, release large amounts of pollutants, and pose a threat to human health. Although deep learning models outperform traditional methods in predicting wildfires, their accuracy drops to about 90% when using remotely sensed data. To effectively monitor and predict fires, this project aims to develop deep learning models capable of processing multivariate remotely sensed global data in real time. This project innovatively uses SimpleGAN, SparseGAN, and CGAN combined with sliding windows for data augmentation. Among these, CGAN demonstrates superior performance. Additionally, for the prediction classification task, U-Net, ConvLSTM, and Attention ConvLSTM are explored, achieving accuracies of 94.53%, 95.85%, and 93.40%, respectively, with ConvLSTM showing the best performance. The study focuses on a region in the Republic of the Congo, where predictions were made and compared with future data. The results showed significant overlap, highlighting the model’s effectiveness. Furthermore, the functionality developed in this study can be extended to medical imaging and other applications involving high-precision remote-sensing images.
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institution Kabale University
issn 2571-6255
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publisher MDPI AG
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series Fire
spelling doaj-art-7d125c174fe94113aaa9b031b1de12b42025-01-24T13:32:18ZengMDPI AGFire2571-62552025-01-01811910.3390/fire8010019Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and MonitoringShiying Yu0Minerva Singh1Department of Earth Science and Engineering, Imperial College London, London SW7 1NE, UKCentre for Environmental Policy, Imperial College London, London SW7 1NE, UKWildfires have significant ecological, social, and economic impacts, release large amounts of pollutants, and pose a threat to human health. Although deep learning models outperform traditional methods in predicting wildfires, their accuracy drops to about 90% when using remotely sensed data. To effectively monitor and predict fires, this project aims to develop deep learning models capable of processing multivariate remotely sensed global data in real time. This project innovatively uses SimpleGAN, SparseGAN, and CGAN combined with sliding windows for data augmentation. Among these, CGAN demonstrates superior performance. Additionally, for the prediction classification task, U-Net, ConvLSTM, and Attention ConvLSTM are explored, achieving accuracies of 94.53%, 95.85%, and 93.40%, respectively, with ConvLSTM showing the best performance. The study focuses on a region in the Republic of the Congo, where predictions were made and compared with future data. The results showed significant overlap, highlighting the model’s effectiveness. Furthermore, the functionality developed in this study can be extended to medical imaging and other applications involving high-precision remote-sensing images.https://www.mdpi.com/2571-6255/8/1/19wildfiredeep learningremote sensingmultivariate datagenerative adversarial network
spellingShingle Shiying Yu
Minerva Singh
Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring
Fire
wildfire
deep learning
remote sensing
multivariate data
generative adversarial network
title Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring
title_full Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring
title_fullStr Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring
title_full_unstemmed Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring
title_short Deep Learning-Based Remote Sensing Image Analysis for Wildfire Risk Evaluation and Monitoring
title_sort deep learning based remote sensing image analysis for wildfire risk evaluation and monitoring
topic wildfire
deep learning
remote sensing
multivariate data
generative adversarial network
url https://www.mdpi.com/2571-6255/8/1/19
work_keys_str_mv AT shiyingyu deeplearningbasedremotesensingimageanalysisforwildfireriskevaluationandmonitoring
AT minervasingh deeplearningbasedremotesensingimageanalysisforwildfireriskevaluationandmonitoring