Explainable AI Methods: Challenges and Opportunities for EO Data Analysis

This chapter explores the importance of XAI in EO data analysis, providing an in-depth review of existing XAI techniques including intrinsic (also called as ante-hoc) and post-hoc methods; these methods are further classified on model-specific and model-agnostic approaches, as well as human-centric explanation techniques. It discusses the challenges associated with applying XAI to EO data and highlights the opportunities presented by emerging advancements in hybrid AI models, physics-informed learning, and human-in-the-loop frameworks. Real-world applications of XAI in EO, including climate change monitoring, land use classification, disaster response, and urban development, are examined to demonstrate its practical benefits. Furthermore, this chapter delves into research directions coming up in the future, emphasizing the need for standardization, benchmarking, and collaborative efforts between AI researchers and EO scientists. By fostering transparency and trust, XAI methods can enhance the reliability and adoption of AI-driven EO solutions, ensuring their ethical and responsible implementation. The valuable insights provided in this chapter serve as a valuable input for researchers, policymakers, and practitioners striving to integrate explainability into EO-based AI systems. We also illustrate the challenges of applying the mentioned XAI methods on EO data analysis.