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ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery

Reiersen, Gyri and Dao, David and Lütjens, Björn and Klemmer, Konstantin and Amara, Kenza and Steinegger, Attila and Zhang, Ce and Zhu, Xiao Xiang (2022) ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. Thirty-Sixth AAAI Conference on Artificial Intelligence, AI for Social Impact Track, 22. Feb. - 01. März 2022, Virtuell.

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Item URL in elib:https://elib.dlr.de/146943/
Document Type:Conference or Workshop Item (Speech)
Title:ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Reiersen, GyriTUMUNSPECIFIED
Dao, DavidETH ZürichUNSPECIFIED
Lütjens, BjörnMITUNSPECIFIED
Klemmer, KonstantinTUMUNSPECIFIED
Amara, KenzaUNSPECIFIEDUNSPECIFIED
Steinegger, AttilaUNSPECIFIEDUNSPECIFIED
Zhang, CeUNSPECIFIEDUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Accepted
Keywords:AI4EO, Dataset, Carbon Stock, Deep Learning, Aerial Imagery
Event Title:Thirty-Sixth AAAI Conference on Artificial Intelligence, AI for Social Impact Track
Event Location:Virtuell
Event Type:international Conference
Event Dates:22. Feb. - 01. März 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:08 Dec 2021 13:06
Last Modified:09 Dec 2021 10:08

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