Hodges, Jonathan L., Brian Y. Lattimer (2019). "Wildland Fire Spread Modeling Using Convolutional Neural Networks". In: Fire Technology 55.6, pp. 2115-2142. ISSN: 1572-8099. DOI: 10.1007/s10694-019-00846-4. URL: https://doi.org/10.1007/s10694-019-00846-4 (visited on 01/16/2023).
Huot, Fantine, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, Yi-Fan Chen (2022). "Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data". In: IEEE Transactions on Geoscience and Remote Sensing 60. Conference Name: IEEE Transactions on Geoscience and Remote Sensing, pp. 1-13. ISSN: 1558-0644. DOI: 10.1109/TGRS.2022.3192974.
Huot, Fantine, R. Lily Hu, Matthias Ihme, Qing Wang, John Burge, Tianjian Lu, Jason Hickey, Yi-Fan Chen, John Anderson (2021). Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data. arXiv: 2010.07445 [cs]. URL: http://arxiv.org/abs/2010.07445 (visited on 01/24/2023).
Iakubovskii, Pavel (2019). Segmentation Models Pytorch. URL: https://github.com/qubvel/segmentation_models.pytorch.
Jeffrey, Ed, Isobel Gray, James O'Connor, Jade Constantinou, Maria Ghironi, Patrick Talon, Robin Cole, Ross Hawton, Tianran Zhang (2023). Satellite Next Day Wildfire Spread. original-date: 2022-01-31T16:29:05Z. URL: https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon (visited on 05/29/2023).
JPL, NASA (2013). "NASA Shuttle Radar Topography Mission Global 1 arc second". In: NASA EOSDIS Land Processes DAAC. DOI: 10.5067/MEaSUREs/SRTM/SRTMGL1.003.
Kang, Bingyi, Yu Li, Sa Xie, Zehuan Yuan, Jiashi Feng (2021). "Exploring Balanced Feature Spaces for Representation Learning". In: International Conference on Learning Representations. URL: https://openreview.net/forum?id=OqtLIabPTit.
Khennou, Fadoua, Jade Ghaoui, Moulay A. Akhloufi (2021). "Forest fire spread prediction using deep learning". In: Geospatial Informatics XI. Geospatial Informatics XI. Ed. by Kannappan Palaniappan, Gunasekaran Seetharaman, Joshua D. Harguess. Online Only, United States: SPIE, p. 16. ISBN: 978-1-5106-4303-1 978-1-5106-4304-8. DOI: 10.1117/12.2585997. URL: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11733/2585997/Forest-fire-spread-prediction-using-deep-learning/10.1117/12.2585997.full (visited on 11/15/2022).
Kondylatos, Spyros, Ioannis Prapas, Michele Ronco, Ioannis Papoutsis, Gustau Camps-Valls, María Piles, Miguel-Ángel Fernández-Torres, Nuno Carvalhais (2022). "Wildfire Danger Prediction and Understanding With Deep Learning". In: Geophysical Research Letters 49.17, e2022GL099368. ISSN: 1944-8007. DOI: 10.1029/2022GL099368. URL: https://onlinelibrary.wiley.com/doi/abs/10.1029/2022GL099368 (visited on 12/05/2022).
Loshchilov, Ilya, Frank Hutter (2019). "Decoupled Weight Decay Regularization". In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. URL: https://openreview.net/forum?id=Bkg6RiCqY7.
Ntinas, Vasileios G., Byron E. Moutafis, Giuseppe A. Trunfio, Georgios Ch. Sirakoulis (2017). "Parallel fuzzy cellular automata for data-driven simulation of wildfire spreading". In: Journal of Computational Science 21, pp. 469-485. ISSN: 1877-7503. DOI: 10.1016/j.jocs.2016.08.003. URL: https://www.sciencedirect.com/science/article/pii/S1877750316301260 (visited on 01/18/2023).
Oliva, Patricia, Wilfrid Schroeder (2015). "Assessment of VIIRS  $375\mathrm{m}$  active fire detection product for direct burned area mapping". In: Remote Sensing of Environment 160, pp. 144-155.