Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth observation (EO) missions, from low-level vision tasks like super-resolution, denoising and inpainting, to high-level vision tasks like scene classification, object detection and semantic segmentation. While AI techniques enable researchers to observe and understand the Earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety-critical. This paper reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning, uncertainty and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors' knowledge, this paper is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the paper to move this vibrant field of research forward.

翻译：近年来人工智能（AI）的显著进展极大推动了地球科学与遥感领域的研究。基于AI的算法，特别是深度学习算法，已被广泛开发并应用于遥感数据分析。AI的成功应用几乎涵盖地球观测（EO）任务的各个方面，从超分辨率重建、去噪和图像修复等低层视觉任务，到场景分类、目标检测和语义分割等高层视觉任务。尽管AI技术使研究者能够更精准地观测和理解地球，但考虑到许多地球科学与遥感任务具有高度安全关键性，AI模型的脆弱性与不确定性值得进一步关注。本文系统梳理了地球科学与遥感领域AI安全的发展现状，涵盖以下五个重要方面：对抗攻击、后门攻击、联邦学习、不确定性与可解释性。此外，本文还探讨了潜在机遇与未来趋势，为后续研究提供参考。据作者所知，本文是首个对地球科学与遥感社区中AI安全相关研究进行系统性综述的工作。文中亦列出相关开源代码与数据集，以推动这一活跃研究领域的持续发展。