Self-supervised contrastive learning for wildfire detection: Utility and limitations

Jae Won Choi, Nicholas LaHaye, Yuzhou Chen, Hugo Kyo Lee, Yulia Gel

Abstract: Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation. However, despite significant advances in terms of performance, its lack of explainability and interpretability, inherent to neural networks in general since their inception, remains a major source of criticism. Hence it comes as no surprise that the expansion of deep learning methods in remote sensing is accompanied by increasingly intensive efforts oriented toward addressing this drawback through the exploration of a wide spectrum of Explainable Artificial Intelligence techniques. This chapter, organized according to prominent Earth observation application fields, presents a panorama of the state-of-the-art in explainable remote sensing image analysis.