Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery-Reference-Cited by-同舟云学术

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Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Published:2020-02-05 Issue:4 Volume:6 Page:472-486
ISSN:2056-3485
Container-title:Remote Sensing in Ecology and Conservation
language:en
Short-container-title:Remote Sens. Ecol.

Author:

Kattenborn Teja¹^ORCID,Eichel Jana²^ORCID,Wiser Susan³^ORCID,Burrows Larry³,Fassnacht Fabian E.¹^ORCID,Schmidtlein Sebastian¹

Affiliation:

1. Institute of Geography and Geoecology Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 76131 Karlsruhe Germany

2. Department of Physical Geography Utrecht University Princetonlaan 8a 3584 CB Utrecht The Netherlands

3. Manaaki Whenua – Landcare Research PO Box 69040 Lincoln 7640 New Zealand

Funder

Royal Society of New Zealand

Publisher

Wiley

Subject

Nature and Landscape Conservation,Computers in Earth Sciences,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.146

Reference77 articles.

1. Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

2. Deep learning for computational biology

3. On scales and dynamics in observing the environment

4. 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology

5. Is Pinus radiata invading the native vegetation in central Chile? Demographic responses in a fragmented forest

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