mlsanityData to 7 Oct 2026

OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation

arXiv 2511.13655 ↗First posted 17 Nov 2025cs.CV cs.LGcited by 20

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Abstract (arXiv)

Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. OlmoEarth achieves state-of-the-art performance compared to 12 other foundation models across a variety of research benchmarks and real-world tasks from external partners. When evaluating embeddings OlmoEarth achieves the best performance on 15 out of 24 tasks, and with full fine-tuning it is the best on 19 of 29 tasks. We deploy OlmoEarth as the backbone of an end-to-end platform for data collection, labeling, training, and inference of Earth observation models. The OlmoEarth Platform puts frontier foundation models and powerful data management tools into the hands of non-profits and NGOs working to solve the world's biggest problems. OlmoEarth source code, training data, and pre-trained weights are available at hrefhttps://github.com/allenai/olmoearthpretrainhttps://github.com/allenai/olmoearthpretrain.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 17 Nov 2025.

Builds on

33 of 54 references.

References · 33

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
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  8. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 982
  9. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 402
  10. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 302
  11. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 231
  12. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 214
21 more
  1. 2020Geography-Aware Self-Supervised Learningcited by 190
  2. 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoderscited by 167
  3. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 164
  4. 2022FlexiViT: One Model for All Patch Sizescited by 151
  5. 2021Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networkscited by 141
  6. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 139
  7. 2023GEO-Bench: Toward Foundation Models for Earth Monitoringcited by 122
  8. 2025AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label datacited by 122
  9. 2018Tile2Vec: Unsupervised representation learning for spatially distributed datacited by 109
  10. 2023Lightweight, Pre-trained Transformers for Remote Sensing Timeseriescited by 102
  11. 2023Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidarcited by 60
  12. 2025Towards a Unified Copernicus Foundation Model for Earth Visioncited by 52
  13. 2025Galileo: Learning Global & Local Features of Many Remote Sensing Modalitiescited by 41
  14. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 35
  15. 2025Panopticon: Advancing Any-Sensor Foundation Models for Earth Observationcited by 34
  16. 2020ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagerycited by 27
  17. 2023Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matterscited by 27
  18. 2025Holistically Evaluating the Environmental Impact of Creating Language Modelscited by 26
  19. 2022Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learningcited by 20
  20. 2024Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learningcited by 20
  21. 2024Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learningcited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 20.

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  1. 2026No One Knows the State of the Art in Geospatial Foundation Modelscited by 9
  2. 2026Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Accesscited by 4
  3. 2026From Pixels to Patches: Pooling Strategies for Earth Embeddingscited by 4
  4. 2026UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observationcited by 4
  5. 2026TESSERA v2: Scaling Pixel-wise Earth Foundation Modelscited by 3
  6. 2026COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Datacited by 1
  7. 2026Agentic AI for Remote Sensing: Technical Challenges and Research Directionscited by 1
  8. 2026OlmoEarth v1.2: A more efficient family of OlmoEarth modelscited by 1
  9. 2026From Machine Learning to Large-Scale EO Products: Best Practices for Making Mapscited by 1
  10. 2026GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentationcited by 1
  11. 2026SLED: Scalable Location Encoding via Distillationcited by 1
  12. 2026Embedding-Only Uplink for Onboard Retrieval Under Shift in Remote Sensingcited by 0
8 more
  1. 2026Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Modelcited by 0
  2. 2026GeoViSTA: Geospatial Vision-Tabular Transformer for Multimodal Environment Representationcited by 0
  3. 2026Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmarkcited by 0
  4. 2026Now We Know? A Systematic Comparison of TerraMind and THORcited by 0
  5. 2026Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscalingcited by 0
  6. 2026Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inferencecited by 0
  7. 2026A Controlled Evaluation of Model Rankings and Input Reliance in Surface Water Segmentationcited by 0
  8. 2026Physically Typed and Geometry-Aware Representations for Earth Foundation Modelscited by 0