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A Deep Learning Approach for Regular Rainforest Monitoring with Sentinel-1 Time Series

Diniz Dal Molin Junior, Ricardo Simao and Rizzoli, Paola and Thirion-Lefevre, Laetitia and Guinvarc’h, Régis (2025) A Deep Learning Approach for Regular Rainforest Monitoring with Sentinel-1 Time Series. Living Planet Symposium, 2025-06-23, Vienna, Austria.

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Item URL in elib:https://elib.dlr.de/214226/
Document Type:Conference or Workshop Item (Speech)
Title:A Deep Learning Approach for Regular Rainforest Monitoring with Sentinel-1 Time Series
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Diniz Dal Molin Junior, Ricardo SimaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Thirion-Lefevre, LaetitiaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Guinvarc’h, RégisUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2025
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Accepted
Keywords:forest monitoring; SAR; Sentinel-1; deep learning
Event Title:Living Planet Symposium
Event Location:Vienna, Austria
Event Type:international Conference
Event Date:23 June 2025
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:28 May 2025 15:52
Last Modified:28 May 2025 15:52

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