Aswini, N., & Uma, S. V. (2021, September). Drone Object Detection Using Deep Learning Algorithms. In 2021 Third Inter-national Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1187-1192). IEEE.
Bahhar, C., Ksibi, A., Ayadi, M., Jamjoom, M. M., Ullah, Z., Soufiene, B. O., & Sakli, H. (2023). Wildfire and smoke detec-tion using staged YOLO model and ensemble CNN. Electronics, 12(1), 228.
Bhatnagar, S., Gill, L., & Ghosh, B. (2020). Drone image segmentation using machine and deep learning for mapping raised bog vegetation communities. Remote Sensing, 12(16), 2602.
Canadian Wildland Fire Information System, Natural Resources Canada. https://cwfis.cfs.nrcan.gc.ca/report (accessed on 24 August 2024).
Choutri, K., Lagha, M., Meshoul, S., Batouche, M., Bouzidi, F., & Charef, W. (2023). Fire detection and geo-localization using uav’s aerial images and yolo-based models. Applied Sciences, 13(20), 11548.
Davis, M., & Shekaramiz, M. (2023). Desert/forest fire detection using machine/deep learning techniques. Fire, 6(11), 418.
Ghali, R., Akhloufi, M. A., & Mseddi, W. S. (2022). Deep learning and transformer approaches for UAV-based wildfire detec-tion and segmentation. Sensors, 22(5), 1977, 10.3390/s22051977.
Guede-Fernández, F., Martins, L., de Almeida, R. V., Gamboa, H., & Vieira, P. (2021). A deep learning based object identifi-cation system for forest fire detection. Fire, 4(4), 75.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
Ibraheem, M. K. I., Mohamed, M. B., & Fakhfakh, A. (2024). Forest Defender Fusion System for Early Detection of Forest Fires. Computers, 13(2), 36.
Idroes, G. M., Maulana, A., Suhendra, R., Lala, A., Karma, T., Kusumo, F., ... & Noviandy, T. R. (2023). TeutongNet: A fine-tuned deep learning model for improved forest fire detection. Leuser Journal of Environmental Studies, 1(1), 1-8. ‏
Jonnalagadda, A. V., & Hashim, H. A. (2024). SegNet: A segmented deep learning based Convolutional Neural Network ap-proach for drones wildfire detection. Remote Sensing Applications: Society and Environment, 34, 101181.
Khan, A., Hassan, B., Khan, S., Ahmed, R., & Abuassba, A. (2022a). DeepFire: A novel dataset and deep transfer learning benchmark for forest fire detection. Mobile Information Systems, 2022(1), 5358359.
Khan, S., & Khan, A. (2022b). Ffirenet: Deep learning based forest fire classification and detection in smart cit-ies. Symmetry, 14(10), 2155.
Lee, W., Kim, S., Lee, Y. T., Lee, H. W., & Choi, M. (2017, January). Deep neural networks for wild fire detection with un-manned aerial vehicle. In 2017 IEEE international conference on consumer electronics (ICCE) (pp. 252-253). IEEE.
Manoj, S., & Valliyammai, C. (2023). Drone network for early warning of forest fire and dynamic fire quenching plan genera-tion. EURASIP Journal on Wireless Communications and Networking, 2023(1), 112.
Mousavi, S., & Ilanloo, A. (2023). Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data. Journal of Fu-ture Sustainability, 3(3), 143-166.
Muhammad, S. S., & Alrikabi, J. M. (2024). Fire Detection by using DenseNet 201 algorithm and Surveillance Cameras imag-es. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(1), 81-91.
Nash, W., Drummond, T., & Birbilis, N. (2018). A review of deep learning in the study of materials degradation. npj Materials Degradation, 2(1), 37.
National Interagency Fire Center (NIFC), (2022). https://www.nifc.gov/fire-information/nfn, (Access on 24 August 2024).
Oom, D., & Pereira, J. M. (2013). Exploratory spatial data analysis of global MODIS active fire data. International Journal of Applied Earth Observation and Geoinformation, 21, 326-340.
Osco, L. P., Junior, J. M., Ramos, A. P. M., de Castro Jorge, L. A., Fatholahi, S. N., de Andrade Silva, J., ... & Li, J. (2021). A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observation and Geoinfor-mation, 102, 102456.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottle-necks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).
Seydi, S. T., Saeidi, V., Kalantar, B., Ueda, N., & Halin, A. A. (2022). Fire‐Net: A Deep Learning Framework for Active Forest Fire Detection. Journal of Sensors, 2022(1), 8044390.
Shamta, I., & Demir, B. E. (2024). Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV. Plos one, 19(3), e0299058. ‏
Simonyan, K. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Supriya, Y., & Gadekallu, T. R. (2023). Particle swarm-based federated learning approach for early detection of forest fires. Sustainability, 15(2), 964.
Tan, M., & Le, Q. (2021, July). Efficientnetv2: Smaller models and faster training. In International conference on machine learning (pp. 10096-10106). PMLR.
Tsalera, E., Papadakis, A., Voyiatzis, I., & Samarakou, M. (2023). CNN-based, contextualized, real-time fire detection in computational resource-constrained environments. Energy Reports, 9, 247-257.
Wildfires, World Health Organization. Available online: https://www.who.int/health-topics/wildfires, (Access on 24 August 2024).
Xiao, S. (2023). Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning. Nonlinear Engineering, 12(1), 20220299.
Zheng, S., Zou, X., Gao, P., Zhang, Q., Hu, F., Zhou, Y., ... & Chen, S. (2024). A forest fire recognition method based on modi-fied deep CNN model. Forests, 15(1), 111.