mlsanityData to 7 Oct 2026

A Survey of Foundation Models for Environmental Science

arXiv 2503.03142 ↗First posted 5 Mar 2025cs.LGcited by 3

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Abstract (arXiv)

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal representations, offer transformative opportunities by integrating diverse data sources, capturing spatiotemporal dependencies, and adapting to a broad range of tasks. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in forward prediction, data generation, data assimilation, downscaling, model ensembling, and decision-making across domains. We also detail the development process of these models, covering data collection, architecture design, training, tuning, and evaluation. By showcasing these emerging methods, we aim to foster interdisciplinary collaboration and advance the integration of cutting-edge machine learning for sustainable solutions in environmental science.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 43 of its 74 references that are in the map. First posted on arXiv 5 Mar 2025.

Builds on

43 of 74 references.

References · 43

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  5. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  6. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  7. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 402
  8. 2022Multimodal Learning with Transformers: A Surveycited by 399
  9. 2023ClimaX: A foundation model for weather and climatecited by 325
  10. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 302
  11. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 250
  12. 2023Neural General Circulation Models for Weather and Climatecited by 232
31 more
  1. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 202
  2. 2020A review on outlier/anomaly detection in time series datacited by 187
  3. 2022Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecastcited by 164
  4. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 160
  5. 2023BioCLIP: A Vision Foundation Model for the Tree of Lifecited by 159
  6. 2023Foundation Models for Generalist Geospatial Artificial Intelligencecited by 143
  7. 2023DiffusionSat: A Generative Foundation Model for Satellite Imagerycited by 130
  8. 2023GEO-Bench: Toward Foundation Models for Earth Monitoringcited by 122
  9. 2024RSMamba: Remote Sensing Image Classification with State Space Modelcited by 95
  10. 2023A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4cited by 81
  11. 2023K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilizationcited by 69
  12. 2024Scaling Wearable Foundation Modelscited by 57
  13. 2018Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profilescited by 55
  14. 2017Machine Learning for the Geosciences: Challenges and Opportunitiescited by 53
  15. 2020Deep Learning for Post-Processing Ensemble Weather Forecastscited by 52
  16. 2023GeoGalactica: A Scientific Large Language Model in Geosciencecited by 32
  17. 2024On the Foundations of Earth and Climate Foundation Modelscited by 31
  18. 2023AI Foundation Models for Weather and Climate: Applications, Design, and Implementationcited by 19
  19. 2021Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoringcited by 16
  20. 2021Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrologycited by 15
  21. 2023On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applicationscited by 15
  22. 2024Knowledge-guided Machine Learning: Current Trends and Future Prospectscited by 13
  23. 2025Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Sciencecited by 12
  24. 2020Forecasting Corn Yield with Machine Learning Ensemblescited by 10
  25. 2024Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mappingcited by 9
  26. 2024Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrationscited by 9
  27. 2022AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamicscited by 8
  28. 2023FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystemscited by 8
  29. 2018Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Predictioncited by 7
  30. 2024LITE: Modeling Environmental Ecosystems with Multimodal Large Language Modelscited by 7
  31. 2024EnviroExam: Benchmarking Environmental Science Knowledge of Large Language Modelscited by 3

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Foundation Models for Environmental Science: A Survey of Emerging Frontierscited by 4
  2. 2025Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Frameworkcited by 2
  3. 2025Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scalescited by 0