mlsanityData to 7 Oct 2026

AI Security for Geoscience and Remote Sensing: Challenges and Future Trends

arXiv 2212.09360 ↗First posted 19 Dec 2022cs.CVcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 59 of its 160 references that are in the map. First posted on arXiv 19 Dec 2022.

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59 of 160 references.

References · 59

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 8; 2025: 3; 2026: 5.

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  1. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  2. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectivescited by 27
  3. 2024SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensingcited by 10
  4. 2023GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detectioncited by 9
  5. 2023Threatening Patch Attacks on Object Detection in Optical Remote Sensing Imagescited by 8
  6. 2024C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Imagescited by 7
  7. 2024Dual Selective Fusion Transformer Network for Hyperspectral Image Classificationcited by 7
  8. 2023A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarkingcited by 5
  9. 2023Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Modelscited by 5
  10. 2024Responsible AI for Earth Observationcited by 4
  11. 2023Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classificationcited by 2
  12. 2024Cloud Adversarial Example Generation for Remote Sensing Image Classificationcited by 2
11 more
  1. 2024Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATRcited by 1
  2. 2025On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensingcited by 1
  3. 2026Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusioncited by 1
  4. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  5. 2023Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?cited by 0
  6. 2024Elevator, Escalator, or Neither? Classifying Conveyor State Using Smartphone under Arbitrary Pedestrian Behaviorcited by 0
  7. 2025Revisiting Adversarial Training under Hyperspectral Imagecited by 0
  8. 2025SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusioncited by 0
  9. 2026FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentationcited by 0
  10. 2026AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Modelscited by 0
  11. 2026On the Adversarial Robustness of Remote Sensing Semantic Change Detectioncited by 0