# One for All: Toward Unified Foundation Models for Earth Vision

arXiv: https://arxiv.org/abs/2401.07527 · HTML: https://mlsanity.com/paper/2401.07527/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery](https://mlsanity.com/paper/2312.10115/) (2023)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery](https://mlsanity.com/paper/2209.00727/) (2022)
- [HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods](https://mlsanity.com/paper/2306.00385/) (2023)
- [Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images](https://mlsanity.com/paper/2205.02049/) (2022)
- [THE Benchmark: Transferable Representation Learning for Monocular Height Estimation](https://mlsanity.com/paper/2112.14985/) (2021)

## Built on by (most cited first, 30 of 34)

- [Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation](https://mlsanity.com/paper/2403.15356/) (2024)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Towards a Unified Copernicus Foundation Model for Earth Vision](https://mlsanity.com/paper/2503.11849/) (2025)
- [TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis](https://mlsanity.com/paper/2506.20380/) (2025)
- [Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.10845/) (2025)
- [On the Foundations of Earth and Climate Foundation Models](https://mlsanity.com/paper/2405.04285/) (2024)
- [Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining](https://mlsanity.com/paper/2405.20462/) (2024)
- [SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing](https://mlsanity.com/paper/2507.13812/) (2025)
- [SARATR-X: Toward Building A Foundation Model for SAR Target Recognition](https://mlsanity.com/paper/2405.09365/) (2024)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation](https://mlsanity.com/paper/2504.03166/) (2025)
- [Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality](https://mlsanity.com/paper/2603.00988/) (2026)
- [Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection](https://mlsanity.com/paper/2503.09493/) (2025)
- [Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI](https://mlsanity.com/paper/2406.18295/) (2024)
- [DOFA-CLIP: Multimodal Vision-Language Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.06312/) (2025)
- [MSSDF: Modality-Shared Self-supervised Distillation for High-Resolution Multi-modal Remote Sensing Image Learning](https://mlsanity.com/paper/2506.09327/) (2025)
- [SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction from SAR Imagery](https://mlsanity.com/paper/2507.01747/) (2025)
- [Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration](https://mlsanity.com/paper/2510.19579/) (2025)
- [Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation](https://mlsanity.com/paper/2602.19863/) (2026)
- [HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data](https://mlsanity.com/paper/2604.04306/) (2026)
- [Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier](https://mlsanity.com/paper/2405.15780/) (2024)
- [SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models](https://mlsanity.com/paper/2406.06776/) (2024)
- [CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models](https://mlsanity.com/paper/2502.13734/) (2025)
- [Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification](https://mlsanity.com/paper/2505.15334/) (2025)
- [Distributed Cross-Channel Hierarchical Aggregation for Foundation Models](https://mlsanity.com/paper/2506.21411/) (2025)
- [AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data?](https://mlsanity.com/paper/2508.06057/) (2025)
- [COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets](https://mlsanity.com/paper/2508.09886/) (2025)
- [Unifying Heterogeneous Multi-Modal Remote Sensing Detection Via Language-Pivoted Pretraining](https://mlsanity.com/paper/2603.01758/) (2026)
- [Emerging Flexible Designs for Geospatial Multimodal Foundation Models](https://mlsanity.com/paper/2606.12595/) (2026)
- [DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model](https://mlsanity.com/paper/2511.07808/) (2025)

## Abstract (arXiv)

Foundation models characterized by extensive parameters and trained on large-scale datasets have demonstrated remarkable efficacy across various downstream tasks for remote sensing data. Current remote sensing foundation models typically specialize in a single modality or a specific spatial resolution range, limiting their versatility for downstream datasets. While there have been attempts to develop multi-modal remote sensing foundation models, they typically employ separate vision encoders for each modality or spatial resolution, necessitating a switch in backbones contingent upon the input data. To address this issue, we introduce a simple yet effective method, termed OFA-Net (One-For-All Network): employing a single, shared Transformer backbone for multiple data modalities with different spatial resolutions. Using the masked image modeling mechanism, we pre-train a single Transformer backbone on a curated multi-modal dataset with this simple design. Then the backbone model can be used in different downstream tasks, thus forging a path towards a unified foundation backbone model in Earth vision. The proposed method is evaluated on 12 distinct downstream tasks and demonstrates promising performance.