# Vision Foundation Models in Remote Sensing: A Survey

arXiv: https://arxiv.org/abs/2408.03464 · HTML: https://mlsanity.com/paper/2408.03464/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2024.

## Built on by (most cited first, 30 of 54)

- [REOBench: Benchmarking Robustness of Earth Observation Foundation Models](https://mlsanity.com/paper/2505.16793/) (2025)
- [ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild](https://mlsanity.com/paper/2501.13354/) (2025)
- [No One Knows the State of the Art in Geospatial Foundation Models](https://mlsanity.com/paper/2605.12678/) (2026)
- [Geospatial Foundation Models to Enable Progress on Sustainable Development Goals](https://mlsanity.com/paper/2505.24528/) (2025)
- [SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images](https://mlsanity.com/paper/2512.20013/) (2025)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation](https://mlsanity.com/paper/2504.06962/) (2025)
- [Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition](https://mlsanity.com/paper/2410.02069/) (2024)
- [Foundation Models for Environmental Science: A Survey of Emerging Frontiers](https://mlsanity.com/paper/2504.04280/) (2025)
- [CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts](https://mlsanity.com/paper/2509.14104/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks](https://mlsanity.com/paper/2603.09471/) (2026)
- [DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models](https://mlsanity.com/paper/2503.16426/) (2025)
- [DeepAndes: A Self-Supervised Vision Foundation Model for Multi-Spectral Remote Sensing Imagery of the Andes](https://mlsanity.com/paper/2504.20303/) (2025)
- [MSSDF: Modality-Shared Self-supervised Distillation for High-Resolution Multi-modal Remote Sensing Image Learning](https://mlsanity.com/paper/2506.09327/) (2025)
- [Time2Agri: Temporal Pretext Tasks for Agricultural Monitoring](https://mlsanity.com/paper/2507.04366/) (2025)
- [A Sentinel-3 foundation model for ocean colour](https://mlsanity.com/paper/2509.21273/) (2025)
- [Benchmarking DINOv3 for Multi-Task Stroke Analysis on Non-Contrast CT](https://mlsanity.com/paper/2509.23132/) (2025)
- [Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2510.15615/) (2025)
- [DINOv3 Visual Representations for Blueberry Perception Toward Robotic Harvesting](https://mlsanity.com/paper/2603.02419/) (2026)
- [MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications](https://mlsanity.com/paper/2604.02719/) (2026)
- [HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data](https://mlsanity.com/paper/2604.04306/) (2026)
- [Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification](https://mlsanity.com/paper/2505.15334/) (2025)
- [Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges](https://mlsanity.com/paper/2508.06832/) (2025)
- [Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal Gemini 2.5 Model for Remote Sensing Applications](https://mlsanity.com/paper/2509.19087/) (2025)
- [Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users](https://mlsanity.com/paper/2511.01990/) (2025)
- [Learning with less: label-efficient land cover classification at very high spatial resolution using self-supervised deep learning](https://mlsanity.com/paper/2511.03004/) (2025)
- [First On-Orbit Demonstration of a Geospatial Foundation Model](https://mlsanity.com/paper/2512.01181/) (2025)
- [Leveraging AI multimodal geospatial foundation models for improved near-real-time flood mapping at a global scale](https://mlsanity.com/paper/2512.02055/) (2025)
- [Prithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mapping](https://mlsanity.com/paper/2601.02315/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) technologies have profoundly transformed the field of remote sensing, revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, remote sensing research has been significantly enhanced by the advent of foundation models-large-scale, pre-trained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This paper provides a comprehensive survey of foundation models in the remote sensing domain. We categorize these models based on their architectures, pre-training datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those foundation models. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pre-training methods, particularly self-supervised learning techniques like contrastive learning and masked autoencoders, remarkably enhance the performance and robustness of foundation models. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for continued development and application of foundation models in remote sensing.