metadata
license: apache-2.0
library_name: pytorch
tags:
- methane-detection
- hyperspectral
- semantic-segmentation
- remote-sensing
- fourier-neural-operator
- aviris-ng
- emit
FLAME pretrained weights
Weights for FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery.
Code: https://github.com/ROKMC1250/FLAME
| File | Trained on | Config |
|---|---|---|
flame_starcop.pt |
STARCOP | flame_starcop.yaml |
flame_emit.pt |
OxHyperSyntheticCH4 | flame_emit.yaml |
Each checkpoint is a torch.save dict with keys model, epoch and
metric. The matching training configs are included here and in the code
repository under configs/.
Usage
pip install huggingface_hub
hf download hjh1037/FLAME flame_starcop.pt flame_emit.pt --local-dir .
See the code repository for evaluation and visualization instructions.
import torch, yaml
from flame.model import build_model
cfg = yaml.safe_load(open('configs/flame_starcop.yaml'))
model = build_model(cfg['model']).eval()
state = torch.load('flame_starcop.pt', map_location='cpu', weights_only=False)
model.load_state_dict(state['model'])
Citation
@article{heo2026flame,
title={FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery},
author={Heo, Junhyuk and Park, Junhwan and Sim, Sancheol and Choi, Beomkyu and Cho, Woojin},
journal={arXiv preprint arXiv:2606.01577},
year={2026}
}