Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
<|reference_start|>Opening the Black-Box: A Systematic Review on Explainable AI 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 this issue 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. Our review provides a complete summary of the state-of-the-art in the field. 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 of explainable AI in Remote Sensing.<|reference_end|>
@article{höhl2024opening,
    title={Opening the Black-Box: A Systematic Review on Explainable AI in Remote
  Sensing},
    author={Adrian H"ohl, Ivica Obadic, Miguel 'Angel Fern'andez Torres, Hiba
  Najjar, Dario Oliveira, Zeynep Akata, Andreas Dengel, Xiao Xiang Zhu},
    journal={arXiv preprint arXiv:2402.13791},
    year={2024},
    doi={10.1109/MGRS.2024.3467001},
    archivePrefix={arXiv},
    eprint={2402.13791},
    primaryClass={cs.LG}
}