# 3D Cloud reconstruction through geospatially-aware Masked Autoencoders

arXiv: https://arxiv.org/abs/2501.02035 · HTML: https://mlsanity.com/paper/2501.02035/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data](https://mlsanity.com/paper/2604.04306/) (2026)
- [Global 3D Reconstruction of Clouds & Tropical Cyclones](https://mlsanity.com/paper/2511.04773/) (2025)
- [SGMAGNet: A Baseline Model for 3D Cloud Phase Structure Reconstruction on a New Passive Active Satellite Benchmark](https://mlsanity.com/paper/2509.15706/) (2025)
- [CERBERUS: A Three-Headed Decoder for Vertical Cloud Profiles](https://mlsanity.com/paper/2604.08772/) (2026)
- [C3DIR: A Deep Learning 3-Dimensional Cloud Property Retrieval Scheme for Passive Satellite Imagers](https://mlsanity.com/paper/2607.16929/) (2026)

## Abstract (arXiv)

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.