Corpus ID: 236318278

Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery

@article{Reiersen2021TacklingTO,
  title={Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery},
  author={Gyri Reiersen and David Dao and Bj{\"o}rn L{\"u}tjens and Konstantin Klemmer and Xiaoxiang Zhu and Ce Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11320}
}
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is stored in forests is, however, still largely done via expensive, timeconsuming, and sometimes unaccountable field measurements. To overcome these limitations, many verification bodies are leveraging machine learning (ML) algorithms to estimate forest carbon from satellite or aerial imagery. Aerial imagery allows for… Expand

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