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Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning.
Seidel, Dominik; Annighöfer, Peter; Thielman, Anton; Seifert, Quentin Edward; Thauer, Jan-Henrik; Glatthorn, Jonas; Ehbrecht, Martin; Kneib, Thomas; Ammer, Christian.
Afiliación
  • Seidel D; Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany.
  • Annighöfer P; Forest and Agroforest Systems, Technical University of Munich, Freising, Germany.
  • Thielman A; Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany.
  • Seifert QE; Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany.
  • Thauer JH; Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany.
  • Glatthorn J; Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany.
  • Ehbrecht M; Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany.
  • Kneib T; Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany.
  • Ammer C; Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany.
Front Plant Sci ; 12: 635440, 2021.
Article en En | MEDLINE | ID: mdl-33643364
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Plant Sci Año: 2021 Tipo del documento: Article País de afiliación: Alemania
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Plant Sci Año: 2021 Tipo del documento: Article País de afiliación: Alemania