Opening the Black Box: A systematic review on explainable artificial intelligence in remote sensing

In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the used explainable AI methods and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address these issues by performing a systematic review to identify the key trends of how explainable AI is used in remote sensing and shed light on novel explainable AI approaches and emerging directions that tackle specific remote sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights in remote sensing, and reflect on the approaches used for explainable AI methods evaluation. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field.