mlsanityData to 14 Oct 2026

SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing

arXiv 2412.19237 ↗First posted 26 Dec 2024cs.CV cs.LGcited by 11

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Abstract (arXiv)

Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation. Its vast volume, diverse sources, and temporal continuity make it particularly well-suited for developing large Visual Foundation Models (VFMs). These models serve as powerful feature extractors, leveraging extensive RS data for pretraining and subsequent fine-tuning in various geoscientific applications. However, existing VFMs in the RS domain often concentrate on specific image characteristics, neglecting the full season-aware potential of RS data. To bridge this gap, we introduce SeaMo, a novel VFM that effectively integrates multimodal and multi-seasonal RS information. SeaMo leverages a masked image modeling framework to fully exploit the spatial, spectral, and seasonal dimensions of RS data. Specifically, we employ unaligned spatial region selection to capture spatial heterogeneity, incorporate multi-source inputs for enhanced multimodal integration, and introduce temporal-multimodal fusion blocks to assimilate seasonal variations effectively. By explicitly modeling the complex, season-dependent attributes of RS data, SeaMo enhances generalization, robustness, and adaptability across geoscientific tasks. Extensive experiments and ablation studies demonstrate its superior performance, underscoring its potential as a foundational model for Earth observation.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 46 of its 61 references that are in the map. First posted on arXiv 26 Dec 2024.

Builds on

46 of 61 references.

References · 46

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34 more
  1. 2015Unsupervised Learning of Video Representations using LSTMscited by 435
  2. 2022Masked Autoencoders As Spatiotemporal Learnerscited by 429
  3. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 408
  4. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensingcited by 363
  5. 2023ClimaX: A foundation model for weather and climatecited by 337
  6. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 308
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  8. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 252
  9. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 234
  10. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 215
  11. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 205
  12. 2020Geography-Aware Self-Supervised Learningcited by 190
  13. 2023GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localizationcited by 187
  14. 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoderscited by 169
  15. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 167
  16. 2018Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networkscited by 110
  17. 2023Towards Geospatial Foundation Models via Continual Pretrainingcited by 109
  18. 2024Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observationcited by 104
  19. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  20. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 92
  21. 2024Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagerycited by 83
  22. 2022Siamese Image Modeling for Self-Supervised Vision Representation Learningcited by 78
  23. 2023Siamese Masked Autoencoderscited by 76
  24. 2022Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentationcited by 69
  25. 2023Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensingcited by 48
  26. 2024Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensingcited by 47
  27. 2019In-domain representation learning for remote sensingcited by 41
  28. 2022Self-supervised Vision Transformers for Joint SAR-optical Representation Learningcited by 40
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 5.

11 papers.

  1. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 39
  2. 2026Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodalitycited by 12
  3. 2025Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challengescited by 3
  4. 2025Any-Optical-Model: A Universal Foundation Model for Optical Remote Sensingcited by 3
  5. 2025ChatENV: An Interactive Vision-Language Model for Sensor-Guided Environmental Monitoring and Scenario Simulationcited by 2
  6. 2025Hyperspectral Imagingcited by 1
  7. 2025Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environmentscited by 1
  8. 2026AviationLMM: A Large Multimodal Foundation Model for Civil Aviationcited by 0
  9. 2026Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inferencecited by 0
  10. 2026VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimationcited by 0
  11. 2026T-JEPA: A Temporal Joint-Embedding Predictive Architecture for Learning Better Remote Sensing Representationscited by 0