Explainable AI for Earth Observation: Current Methods, Open Challenges,
  and Opportunities
Gulsen Taskin Erchan Aptoula Alp Ertürk
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 being accompanied by
increasingly intensive efforts oriented towards 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.