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Earth observations, especially satellite data, have produced a wealth of methods and results in meeting global challenges, often presented in unstructured texts such as papers or reports. Accurate extraction of satellite and instrument entities from these unstructured texts can help to link and reuse Earth observation resources.

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Satellite-Instrument-NER

We introduce how to use the pre-trained language model and distant supervision to detect satellite and instrument entities in unstructured text.

The models currently available for download and online testing include:

satellite-instrument-roberta-NER

satellite-instrument-bert-NER

alt online example

Citation

Our paper has been published in the International Journal of Digital Earth :

@article{lin2022satellite,
  title={Satellite and instrument entity recognition using a pre-trained language model with distant supervision},
  author={Lin, Ming and Jin, Meng and Liu, Yufu and Bai, Yuqi},
  journal={International Journal of Digital Earth},
  volume={15},
  number={1},
  pages={1290--1304},
  year={2022},
  publisher={Taylor \& Francis}
}

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Earth observations, especially satellite data, have produced a wealth of methods and results in meeting global challenges, often presented in unstructured texts such as papers or reports. Accurate extraction of satellite and instrument entities from these unstructured texts can help to link and reuse Earth observation resources.

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