GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery
dc.contributor.author
Dao, David
dc.contributor.author
Cang, Catherine
dc.contributor.author
Fung, Clement
dc.contributor.author
Zhang, Ming
dc.contributor.author
Pawlowski, Nick
dc.contributor.author
Gonzales, Reuven
dc.contributor.author
Beglinger, Nick
dc.contributor.author
Zhang, Ce
dc.date.accessioned
2020-05-20T08:23:40Z
dc.date.available
2020-01-29T11:09:11Z
dc.date.available
2020-05-20T08:23:40Z
dc.date.issued
2019
dc.identifier.uri
http://hdl.handle.net/20.500.11850/395334
dc.language.iso
en
en_US
dc.publisher
ICML 2019 Workshop
en_US
dc.title
GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery
en_US
dc.type
Conference Paper
ethz.book.title
Proceedings of the ICML Climate Change Workshop at 36th International Conference on Machine Learning,
en_US
ethz.size
3 p.
en_US
ethz.event
ICML 2019 Workshop Climate Change: How Can AI Help?
en_US
ethz.event.location
Long Beach, CA, USA
en_US
ethz.event.date
June 14, 2019
en_US
ethz.notes
Online proceedings
en_US
ethz.publication.place
s.l.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02663 - Institut für Computing Platforms / Institute for Computing Platforms::09588 - Zhang, Ce (ehemalig) / Zhang, Ce (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02663 - Institut für Computing Platforms / Institute for Computing Platforms::09588 - Zhang, Ce (ehemalig) / Zhang, Ce (former)
en_US
ethz.relation.isPartOf
https://www.climatechange.ai/ICML2019_workshop.html#Ideas-Track
ethz.date.deposited
2020-01-29T11:09:18Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
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ethz.rosetta.installDate
2020-05-20T08:23:51Z
ethz.rosetta.lastUpdated
2024-02-02T10:55:27Z
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true
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Conference Paper [35477]