3D Cloud reconstruction through geospatially-aware Masked Autoencoders
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Girtsou, Stella Salas-Porras, Emiliano Diaz Freischem, Lilli Massant, Joppe Bintsi, Kyriaki-Margarita Castiglione, Guiseppe Jones, William Eisinger, Michael Johnson, Emmanuel Jungbluth, Anna |
| author_facet | Girtsou, Stella Salas-Porras, Emiliano Diaz Freischem, Lilli Massant, Joppe Bintsi, Kyriaki-Margarita Castiglione, Guiseppe Jones, William Eisinger, Michael Johnson, Emmanuel Jungbluth, Anna |
| contents | Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Cloud reconstruction through geospatially-aware Masked Autoencoders Girtsou, Stella Salas-Porras, Emiliano Diaz Freischem, Lilli Massant, Joppe Bintsi, Kyriaki-Margarita Castiglione, Guiseppe Jones, William Eisinger, Michael Johnson, Emmanuel Jungbluth, Anna Computer Vision and Pattern Recognition Artificial Intelligence 68T45 Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction. |
| title | 3D Cloud reconstruction through geospatially-aware Masked Autoencoders |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45 |
| url | https://arxiv.org/abs/2501.02035 |