---
license: apache-2.0
---

# 🛰️ Model Card: `downstream-satvision-toa-3dclouds`

## ⚙️ Model Overview
- **Model Name**: `downstream-satvision-toa-3dclouds`
- **Base Model**: SatVision-TOA (Giant, 3B parameters)  
- **Architecture**: SwinV2 Transformer (ViT backbone)  
- **Pretraining Objective**: Masked Image Modeling (MIM)  
- **Pretraining Dataset**: 100M globally-distributed MODIS TOA image chips across 14 bands  
- **Resolution**: 128×128 px at ~1 km  
- **Pretraining Conditions**: All-sky (cloud, aerosol, ocean, land)

## 🗂️ Intended Use
- **Task**: 3D cloud vertical reconstruction from satellite TOA imagery
- **Downstream Data**: GOES-ABI chips paired with CloudSat/CALIPSO cloud curtain observations
- **Output**: Per-pixel cloud vertical class (e.g., cloud top/base detection, multilayer structure)

## 🧠 Strengths
- Learns spatial-spectral relationships across diverse global conditions
- Generalizes well across sensors (MODIS → GOES-ABI)
- Outperforms baseline on thin, multilayer, and obscured clouds
- Pretraining improves sample efficiency for fine-tuning

## ⚠️ Limitations
- **Temporal bias**: Terra-MODIS sampling (~9 AM local) may limit temporal generalization
- **Resolution**: Only supports ~1 km scale chips; sub-km cloud structures not resolved
- **Sensor adaptation**: While GOES-ABI is supported, optimal results may require minor domain tuning

## 🛠️ Fine-Tuning & Usage
- **Decoder**: Lightweight FCN head on frozen SatVision-TOA encoder
- **Training Data**: ~7,000 labeled GOES-ABI chips aligned with CloudSat/CALIPSO
- **Validation Set**: 1,300 chips
- **Typical Inference Output**: 2D maps of vertical cloud structure per chip

## 🔁 Adaptation Ideas
- Extend to aerosol, water vapor, or ice phase classification
- Fine-tune on nighttime or different orbital sensors (e.g., VIIRS, Himawari)
- Use as encoder backbone for multitask satellite cloud analysis

## 📝 Citation
If you use this model, please cite:

@article{satvision2024,
title={SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery},
author={Zhu, Le and Caraballo-Vega, Jordan and Gentine, Pierre and Tao, Wenzhong and et al.},
journal={arXiv preprint arXiv:2406.06561},
year={2024}
}


## 🔗 Resources
- [🗃️ Hugging Face Model Repo](https://huggingface.co/nasa-cisto-data-science-group/downstream-satvision-toa-3dclouds)
- [📄 SatVision-TOA Preprint](https://arxiv.org/abs/2406.06561)
- [🌐 Earthdata Overview](https://earthdata.nasa.gov/learn/articles/satvision)

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📌 **Summary**:  
This model leverages a powerful foundation transformer trained on MODIS TOA data to deliver high-fidelity 3D cloud reconstructions from GOES-ABI imagery. It serves as a critical step toward operational cloud analysis from geostationary satellites using foundation model paradigms.