FR1.R10.2

ACTIVE LEARNING GUIDED FINE-TUNING FOR ENHANCING SELF-SUPERVISED BASED MULTI-LABEL CLASSIFICATION OF REMOTE SENSING IMAGES

Lars Möllenbrok, Begüm Demir, Technische Universität Berlin, Germany
Session:
FR1.R10: Label-efficient Learning for Earth Observation Oral
Track:
AI and Big Data
Location:
Room 101
Presentation Time:
Fri, 21 Jul, 08:42 - 08:54 Pacific Time (UTC -8)
Session Co-Chairs:
Ronny Hänsch, German Aerospace Center and Begüm Demir, Technische Universität Berlin
Session Manager:
Rahisha Thottolil
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Session FR1.R10
FR1.R10.1: AUTOMATING IMAGE LABELING FOR REMOTE SENSING USING CADASTRAL DATABASE AND VIDEO GAME ENGINE SIMULATION
Antoine Carreaud, Yves Deillon, Adrien Gressin, University of Applied Sciences Western Switzerland, Switzerland
FR1.R10.2: ACTIVE LEARNING GUIDED FINE-TUNING FOR ENHANCING SELF-SUPERVISED BASED MULTI-LABEL CLASSIFICATION OF REMOTE SENSING IMAGES
Lars Möllenbrok, Begüm Demir, Technische Universität Berlin, Germany
FR1.R10.3: TASK AGNOSTIC COST PREDICTION MODULE FOR SEMANTIC LABELING IN ACTIVE LEARNING
Srikumar Sastry, Nathan Jacobs, Washington University in St. Louis, United States; Mariana Belgiu, Raian Vargas Maretto, ITC Faculty Geo-Information Science and Earth Observation, Netherlands
FR1.R10.4: ANNOTATION COST EFFICIENT ACTIVE LEARNING FOR REMOTE SENSING IMAGE RETRIEVAL
Julia Henkel, Genc Hoxha, Gencer Sumbul, Lars Möllenbrok, Begüm Demir, Technische Universität Berlin, Germany
FR1.R10.5: Have Foundational Models Seen Satellite Images?
Akash Panigrahi, Sagar Verma, Matthieu Terris, Granular AI, India; Maria Vakalopoulou, CentraleSupelec, France
Resources
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