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Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.
Li, Sizhuo; Brandt, Martin; Fensholt, Rasmus; Kariryaa, Ankit; Igel, Christian; Gieseke, Fabian; Nord-Larsen, Thomas; Oehmcke, Stefan; Carlsen, Ask Holm; Junttila, Samuli; Tong, Xiaoye; d'Aspremont, Alexandre; Ciais, Philippe.
Afiliação
  • Li S; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
  • Brandt M; Département Sciences de la terre et de l'univers, espace, Université Paris-Saclay, Gif-sur-Yvette 91190, France.
  • Fensholt R; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
  • Kariryaa A; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
  • Igel C; Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark.
  • Gieseke F; Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark.
  • Nord-Larsen T; Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark.
  • Oehmcke S; Department of Information Systems, University of Münster, Münster 48149, Germany.
  • Carlsen AH; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
  • Junttila S; Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark.
  • Tong X; Department of Earth Observations, The Danish Agency for Data Supply and Infrastructure, Copenhagen 2400, Denmark.
  • d'Aspremont A; Department of Forest Sciences, University of Eastern Finland, Joensuu 80101, Finland.
  • Ciais P; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
PNAS Nexus ; 2(4): pgad076, 2023 Apr.
Article em En | MEDLINE | ID: mdl-37065619
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article