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A Deep Learning Framework for Regularly Monitoring the Amazon Forest with Sentinel-1 InSAR data: Seasonal Challenges and Insights

Diniz Dal Molin Junior, Ricardo Simao and Rizzoli, Paola and Thirion-Lefevre, Laetitia and Guinvarc’h, Régis (2023) A Deep Learning Framework for Regularly Monitoring the Amazon Forest with Sentinel-1 InSAR data: Seasonal Challenges and Insights. ESA FRINGE Workshop, 2023-09-11 - 2023-09-15, Leeds, UK.

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Item URL in elib:https://elib.dlr.de/196403/
Document Type:Conference or Workshop Item (Speech)
Title:A Deep Learning Framework for Regularly Monitoring the Amazon Forest with Sentinel-1 InSAR data: Seasonal Challenges and Insights
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Diniz Dal Molin Junior, Ricardo SimaoUNSPECIFIEDhttps://orcid.org/0000-0003-4103-7910UNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Thirion-Lefevre, LaetitiaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Guinvarc’h, RégisUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:forest mapping; seasonal effects; deep learning; InSAR data
Event Title:ESA FRINGE Workshop
Event Location:Leeds, UK
Event Type:Workshop
Event Dates:2023-09-11 - 2023-09-15
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 - AI4SAR
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Diniz Dal Molin Junior, Ricardo Simao
Deposited On:07 Aug 2023 08:31
Last Modified:06 Dec 2023 19:15

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