Araştırma Makalesi
BibTex RIS Kaynak Göster

YANGINLARDA YANMIŞ ALANLARIN BELİRLENMESİ VE ŞİDDET DEĞERLENDİRMESİ İÇİN ÇİFT ADIMLI DERİN ÖĞRENME ÇERÇEVESİNİN KARŞILAŞTIRMALI BİR ÇALIŞMASI

Yıl 2025, Cilt: 28 Sayı: 1, 513 - 523, 03.03.2025

Murat Mert Yurdakul , Bülent Bayram , Tolga Bakırman , Hamza Osman İlhan

Öz

Orman yangınlarının daha sık ve yoğun hale gelmesiyle birlikte, doğru tespit ve hasar değerlendirmesi için gelişmiş tekniklerin geliştirilmesi büyük önem taşımaktadır. Bu araştırma, yanmış alanları belirlemek ve yangın şiddetini tahmin etmek için MultiResUNet dahil olmak üzere çeşitli U-Net modellerini kullanan Çift Aşamalı Derin Öğrenme Çerçevesi'ni incelemektedir. Uydu görüntülerinden elde edilen maske çıktıları üzerinde, özellikle 4 ve 5 şiddet seviyelerine odaklanılarak, farklı şiddet seviyelerinin etkileri detaylı bir şekilde incelenmiştir. Ayrıca, Mask R-CNN modeli, önceden eğitilmiş ağırlıklar ve sınırlı spektral girdiler nedeniyle görüntü segmentasyonunda yaşanan zorlukları göstermek için bağımsız olarak incelenmiştir. Yapılan analizler, şiddet aralıklarının granülerliğindeki değişikliklerin model performansını nasıl etkilediğini göstererek, yangın değerlendirmesi için daha ayrıntılı şiddet segmentasyonunun faydalarına dair önemli bilgiler sağlamaktadır. Bu yaklaşım, hasar değerlendirmelerinin doğruluğunu artırma ve yangın yönetimi ile müdahalesinde daha bilinçli kararlar alınmasını destekleme potansiyeline sahiptir.

Anahtar Kelimeler

yangın şiddeti tahmini derin sinir ağları çift adımlı mimari uydu görüntü analizi

Destekleyen Kurum

TÜBİTAK

Proje Numarası

122N254

Kaynakça

  • Colomba, L., Farasin, A., Monaco, S., Greco, S., Garza, P., Apiletti, D., Baralis, E., & Cerquitelli, T. (2022). A dataset for burned area delineation and severity estimation from satellite imagery. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22) (pp. 3893–3897). Association for Computing Machinery. https://doi.org/10.1145/3511808.3557528
  • Farasin, A., Colomba, L., & Garza, P. (2020). Double-Step U-Net: A deep learning-based approach for the estimation of wildfire damage severity through Sentinel-2 satellite data. Applied Sciences, 10(12), 4332. https://doi.org/10.3390/app10124332
  • Finney, M. A. (1998). FARSITE: Fire Area Simulator—Model Development and Evaluation (Research Paper RMRS-RP-4, Revised 2004). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
  • Han, Y., Zheng, C., Liu, X., Tian, Y., & Dong, Z. (2024). Burned area and burn severity mapping with a transformer-based change detection model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 13866–13880. https://doi.org/10.1109/JSTARS.2024.3435857
  • He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969. https://doi.org/10.1109/ICCV.2017.322
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
  • Ibtehaz, N., & Rahman, M. S. (2020). Multiresunet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74–87. https://doi.org/10.1016/j.neunet.2019.08.025
  • Kamal, U., Tonmoy, T. I., Das, S., & Hasan, M. K. (2020). Automatic traffic sign detection and recognition using SegU-Net and a modified Tversky loss function with L1-constraint. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1467–1479. https://doi.org/10.1109/TITS.2019.2911727
  • Khennou, F., & Akhloufi, M. A. (2023). Improving wildland fire spread prediction using deep U-Nets. Science of Remote Sensing, 8, 100101. https://doi.org/10.1016/j.srs.2023.100101
  • Li, M., Zhang, Y., Xin, J., Mu, L., Yu, Z., Liu, H., Xie, G., Jiao, S., & Yi, Y. (2021). Early forest fire segmentation based on deep learning. In 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) (pp. 1–5). IEEE. https://doi.org/10.1109/SAFEPROCESS52771.2021.9693660
  • Monaco, S., Greco, S., Farasin, A., Colomba, L., Apiletti, D., Garza, P., Cerquitelli, T., & Baralis, E. (2021). Attention to fires: Multi-channel deep learning models for wildfire severity prediction. Applied Sciences, 11(22), 11060. https://doi.org/10.3390/app112211060
  • Navarro, G., Caballero, I., Silva, G., Parra, P.-C., Vázquez, Á., & Caldeira, R. (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58, 97–106. https://doi.org/10.1016/j.jag.2017.02.003
  • Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv preprint, arXiv:1804.03999
  • Pinto, M. M., Libonati, R., Trigo, R. M., Trigo, I. F., & DaCamara, C. C. (2020). A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 260-274. https://doi.org/10.1016/j.isprsjprs.2019.12.014
  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (Vol. 28).
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28
  • Shamsoshoara, A., Afghah, F., Razi, A., Zheng, L., Fulé, P. Z., & Blasch, E. (2020). Aerial imagery pile burn detection using deep learning: The FLAME dataset. arXiv. https://arxiv.org/abs/2012.14036
  • Wang, Z., Yang, P., Liang, H., Zheng, C., Yin, J., Tian, Y., & Cui, W. (2022). Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sensing, 14(1), 45. https://doi.org/10.3390/rs14010045
  • Zou, Y., Sadeghi, M., Liu, Y., Puchko, A., Le, S., Chen, Y., Andela, N., & Gentine, P. (2023). Attention-Based Wildland Fire Spread Modeling Using Fire-Tracking Satellite Observations. Fire, 6(8), 289. https://doi.org/10.3390/fire6080289
  • Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 (pp. 3-11). Springer International Publishing. https://doi.org/10.1007/978-3-030-00889-5_1

A COMPARATIVE STUDY OF DOUBLE-STEP DEEP LEARNING FRAMEWORK FOR BURNED AREA IDENTIFICATION AND SEVERITY ASSESSMENT IN WILDFIRES

Yıl 2025, Cilt: 28 Sayı: 1, 513 - 523, 03.03.2025

Murat Mert Yurdakul , Bülent Bayram , Tolga Bakırman , Hamza Osman İlhan

Öz

As wildfires become more frequent and intense, it is essential to develop sophisticated techniques for precise detection and damage evaluation. This research examines a Double-Step Deep Learning Framework using several U-Net models, including MultiResUNet, to identify burned areas and estimate severity. Using satellite images, the study explores the effect of different severity levels within mask output, focusing on both 4 and 5 level severity classifications. Additionally, the Mask R-CNN model was evaluated independently for image segmentation, revealing challenges due to its reliance on pretrained weights and limited spectral input. The comparative analysis illustrates how changes in the granularity of severity intervals influence model performance, providing insights into the benefits of more nuanced severity segmentation for wildfire assessment. This approach has the potential to improve the precision of damage assessments and support more informed decision-making in the management and response of wildfires.

Anahtar Kelimeler

wildfire severity prediction deep neural networks double-step architecture satellite imagery analysis

Destekleyen Kurum

TUBITAK

Proje Numarası

122N254

Kaynakça

  • Colomba, L., Farasin, A., Monaco, S., Greco, S., Garza, P., Apiletti, D., Baralis, E., & Cerquitelli, T. (2022). A dataset for burned area delineation and severity estimation from satellite imagery. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22) (pp. 3893–3897). Association for Computing Machinery. https://doi.org/10.1145/3511808.3557528
  • Farasin, A., Colomba, L., & Garza, P. (2020). Double-Step U-Net: A deep learning-based approach for the estimation of wildfire damage severity through Sentinel-2 satellite data. Applied Sciences, 10(12), 4332. https://doi.org/10.3390/app10124332
  • Finney, M. A. (1998). FARSITE: Fire Area Simulator—Model Development and Evaluation (Research Paper RMRS-RP-4, Revised 2004). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
  • Han, Y., Zheng, C., Liu, X., Tian, Y., & Dong, Z. (2024). Burned area and burn severity mapping with a transformer-based change detection model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 13866–13880. https://doi.org/10.1109/JSTARS.2024.3435857
  • He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969. https://doi.org/10.1109/ICCV.2017.322
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
  • Ibtehaz, N., & Rahman, M. S. (2020). Multiresunet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74–87. https://doi.org/10.1016/j.neunet.2019.08.025
  • Kamal, U., Tonmoy, T. I., Das, S., & Hasan, M. K. (2020). Automatic traffic sign detection and recognition using SegU-Net and a modified Tversky loss function with L1-constraint. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1467–1479. https://doi.org/10.1109/TITS.2019.2911727
  • Khennou, F., & Akhloufi, M. A. (2023). Improving wildland fire spread prediction using deep U-Nets. Science of Remote Sensing, 8, 100101. https://doi.org/10.1016/j.srs.2023.100101
  • Li, M., Zhang, Y., Xin, J., Mu, L., Yu, Z., Liu, H., Xie, G., Jiao, S., & Yi, Y. (2021). Early forest fire segmentation based on deep learning. In 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) (pp. 1–5). IEEE. https://doi.org/10.1109/SAFEPROCESS52771.2021.9693660
  • Monaco, S., Greco, S., Farasin, A., Colomba, L., Apiletti, D., Garza, P., Cerquitelli, T., & Baralis, E. (2021). Attention to fires: Multi-channel deep learning models for wildfire severity prediction. Applied Sciences, 11(22), 11060. https://doi.org/10.3390/app112211060
  • Navarro, G., Caballero, I., Silva, G., Parra, P.-C., Vázquez, Á., & Caldeira, R. (2017). Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58, 97–106. https://doi.org/10.1016/j.jag.2017.02.003
  • Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv preprint, arXiv:1804.03999
  • Pinto, M. M., Libonati, R., Trigo, R. M., Trigo, I. F., & DaCamara, C. C. (2020). A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 260-274. https://doi.org/10.1016/j.isprsjprs.2019.12.014
  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (Vol. 28).
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28
  • Shamsoshoara, A., Afghah, F., Razi, A., Zheng, L., Fulé, P. Z., & Blasch, E. (2020). Aerial imagery pile burn detection using deep learning: The FLAME dataset. arXiv. https://arxiv.org/abs/2012.14036
  • Wang, Z., Yang, P., Liang, H., Zheng, C., Yin, J., Tian, Y., & Cui, W. (2022). Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sensing, 14(1), 45. https://doi.org/10.3390/rs14010045
  • Zou, Y., Sadeghi, M., Liu, Y., Puchko, A., Le, S., Chen, Y., Andela, N., & Gentine, P. (2023). Attention-Based Wildland Fire Spread Modeling Using Fire-Tracking Satellite Observations. Fire, 6(8), 289. https://doi.org/10.3390/fire6080289
  • Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 (pp. 3-11). Springer International Publishing. https://doi.org/10.1007/978-3-030-00889-5_1
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme
Bölüm Bilgisayar Mühendisliği
Yazarlar

Murat Mert Yurdakul 0009-0000-7285-7802

Bülent Bayram 0000-0002-4248-116X

Tolga Bakırman 0000-0001-7828-9666

Hamza Osman İlhan 0000-0002-1753-2703

Proje Numarası 122N254
Yayımlanma Tarihi 3 Mart 2025
Gönderilme Tarihi 14 Aralık 2024
Kabul Tarihi 26 Ocak 2025
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 1

Kaynak Göster

APA Yurdakul, M. M., Bayram, B., Bakırman, T., İlhan, H. O. (2025). A COMPARATIVE STUDY OF DOUBLE-STEP DEEP LEARNING FRAMEWORK FOR BURNED AREA IDENTIFICATION AND SEVERITY ASSESSMENT IN WILDFIRES. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 513-523.
 Kapak Resmi İndir