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Article

Pecha, Marek ; Langford, Zachary ; Horák, David ; Tran Mills, Richard
Wildfires identification: Semantic segmentation using support vector machine classifier. (English). In: Chleboun, J., Kůs, P., Papež, J., Rozložník, M., Segeth, K. and Šístek, J. (eds.): Programs and Algorithms of Numerical Mathematics. Proceedings of Seminar. Jablonec nad Nisou, June 19-24, 2022. Institute of Mathematics CAS, Prague, 2023. pp. 173-186
MSC: 68T09, 68T45, 68W15 | DOI: 10.21136/panm.2022.16
Keywords:
wildfire identification; semantic segmentation; support vector machines; distributed training
Summary:
This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with $\mathcal{l}1$-loss and $\mathcal{l}2$-loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.
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