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UID:314@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20230601T140000
DTEND;TZID=Europe/Paris:20230601T140000
DTSTAMP:20260828T081111Z
URL:https://isdm.umontpellier.fr/events/knowledge-transfer-and-representat
 ion-learning-in-remote-sensing-through-self-supervised-methods/
SUMMARY:Knowledge Transfer and Representation Learning in Remote Sensing th
 rough Self-Supervised Methods
DESCRIPTION:Room 109\, Building 9\, St Eloi campus\n\nMachine Learning in M
 ontpellier\, Theory &amp\; Practice\n\nThe field of remote sensing (RS) ha
 s witnessed remarkable advancements\, but the scarcity of labeled data rem
 ains a challenge. This talk focuses on leveraging transfer learning and se
 lf-supervised learning (SSL) methods to benefit downstream tasks with limi
 ted data by learning representations from large datasets. However\, most p
 re-trained models in RS are based on ImageNet or MS COCO\, which may not c
 apture the spectral and spatial characteristics of RS data. To address thi
 s\, SSL provides a solution by learning useful feature representations fro
 m unlabelled RS data. By considering multimodal data\, particularly optica
 l and radar images\, SSL models can improve accuracy and robustness in RS 
 analysis. The talk aims to shed light on the potential of transfer learnin
 g and SSL methods in RS\, emphasising the importance of considering the mu
 lti-modal nature of RS data.\n\nMachine Learning in Montpellier\, Theory &
 amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Saint Eloi Campus\, Building 9\, Room 109\, Montpellier\, 
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Montpellier\, ;X-APPLE-RADI
 US=100;X-TITLE=Saint Eloi Campus\, Building 9\, Room 109:geo:0,0
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