Developing Explainable Artificial Intelligence Models for Space Science Applications

The integration of explainable artificial intelligence (XAI) in space science has ushered in a new era of transparency and reliability in AI-driven applications. This paper delves into the transformative role of XAI in enhancing various aspects of space missions, from satellite imagery analysis to planetary science and human–AI collaboration. The introduction highlights the imperative of explainability in AI, emphasizing the need for transparent and ethical decision-making in high-stakes space missions. In the background, this paper explores the evolution of AI in space science and the emergence of XAI as a critical field. The challenges posed by the complexity of space data and the stringent reliability and safety requirements are examined, underscoring the necessity of robust and interpretable AI systems. The paper discusses various XAI techniques, including model-agnostic approaches like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), intrinsic methods such as decision trees, and generalized additive models. Visualization tools for XAI, including feature importance plots and heatmaps, are also discussed, demonstrating their role in making AI decisions more interpretable and actionable. Three case studies illustrate the practical applications of XAI in space science: monitoring deforestation in Earth observation, facilitating discoveries in planetary science, and enhancing human–AI collaboration in space missions. These examples showcase how XAI improves transparency and reliability and enables more effective decision-making. Finally, the paper looks toward the future, discussing emerging technologies in XAI and their potential to revolutionize space science. Integrating XAI, human–AI collaboration, NLP advancements, and quantum computing is a key trend in space exploration.