eurocontrol-asu/deepcontrail-samples

DeepContrail sample scenes

Five GOES-16 ABI scenes from the test split of the OpenContrails dataset (20250305 export), converted to the DeepContrail dataset format. They let you try DeepContrail and its pretrained model in a minute, without downloading and converting the full dataset.

import numpy as np
from huggingface_hub import snapshot_download
from deepcontrail.core import false_color_from_spectral, read_band_manifest
from deepcontrail.segmentation import load_pretrained, predict

root = snapshot_download("eurocontrol-asu/deepcontrail-samples", repo_type="dataset")
model = load_pretrained("eurocontrol-asu/deepcontrail-segmentation", revision="v0.1.0")
bands = np.load(f"{root}/spectral_bands/test/1801360214472457069.npy")
composite = false_color_from_spectral(bands, model.config.false_color, read_band_manifest(root))
probabilities = predict(model, composite, tta=model.config.tta)  # (256, 256) in [0, 1]

Content

• spectral_bands/test/<id>.npy: (256, 256, 10) float32 brightness temperatures (K) and their split-window difference; spectral_bands/bands.json names each channel.
• semantic_masks/test/<id>.png: the consensus contrail mask (0 background, 255 contrail).
• instance_masks/test/<id>.npy: each annotator's individual contrails.
• metadata/dataset_info.json: provenance, labels and pixel shift (0.0: the masks are aligned with the images).

The scenes are the one used in the model card plus four with many contrails (726 to 1,538 contrail pixels). They are a sample for trying the library, not a benchmark.

Licence and attribution

The data comes from the OpenContrails dataset by Google Research, under the CC BY 4.0 licence; only the file format was changed (by the DeepContrail converter). GOES-16 imagery is courtesy of NOAA. If you use it, please cite:

@article{Ng2024ContrailDO,
  title={Contrail Detection on GOES-16 ABI With the OpenContrails Dataset},
  author={Joe Yue-Hei Ng and Kevin McCloskey and Jian Cui and Vincent R. Meijer and Erica Brand and Aaron Sarna and Nita Goyal and Christopher Van Arsdale and Scott Geraedts},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2024},
  volume={62},
  pages={1-14},
  doi={10.1109/TGRS.2023.3345226}
}