Explainable PostHoc Approaches for EO Data Analysis: Opportunities and Challenges

In the recent past, with growing capabilities of deep learning models like GAN, auto-encoders etc., there has been a phenomenal growth in Earth Observations (EO) data analysis and fields of study like climate change analysis, disaster management, and land use mapping. In addition, even the DL models for a given problem would have a complex architecture making it very hard to decipher and win user trust. This requires Explainable AI methods to make the model interpretable. This chapter explores the landscape of explainable post-hoc methods for EO data analysis, offering a comprehensive overview of techniques such as feature attribution, visualization-based methods, surrogate models, and rule extraction. The chapter also addresses critical challenges, including scalability to large datasets, balancing model accuracy and interpretability, and mitigating risks of misinterpretation. Furthermore, the chapter identifies opportunities to enhance explainability in EO data analysis, emphasizing the importance of trust and accessibility for diverse stakeholders. Finally, emerging trends, such as hybrid explainability approaches and real-time systems, are discussed, outlining a pathway for advancing transparent and actionable EO insights.