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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TH1.O-12.4

ARTIFIVE-POTSDAM: A BENCHMARK FOR LEARNING WITH ARTIFICIAL OBJECTS FOR IMPROVED AERIAL VEHICLE DETECTION

Immanuel Weber, Jens Bongartz, University of Applied Sciences Koblenz, Germany; Ribana Roscher, University of Bonn, Germany
Session:
Machine Learning Datasets in Remote Sensing
Track:
Invited Sessions
Presentation Time:
Thu, 15 Jul, 09:15-09:30 (UTC)
Thu, 15 Jul, 11:15-11:30 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 17:15-17:30 China Standard Time (UTC +8)
Thu, 15 Jul, 05:15-05:30 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Michael Schmitt, Munich University of Applied Sciences and Ronny Hänsch, German Aerospace Center
Session Manager:
Laurens Diels
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Session TH1.O-12
TH1.O-12.1: THERE IS NO DATA LIKE MORE DATA - CURRENT STATUS OF MACHINE LEARNING DATASETS IN REMOTE SENSING
Michael Schmitt, Munich University of Applied Sciences, Germany; Seyed Ali Ahmadi, K. N. Toosi University of Technology, Iran; Ronny Hänsch, German Aerospace Center (DLR), Germany
TH1.O-12.3: Toward Dataset Construction for Remote Sensing Image Interpretation
Yang Long, Gui-Song Xia, Wen Yang, Liangpei Zhang, Deren Li, Wuhan University, China
TH1.O-12.4: ARTIFIVE-POTSDAM: A BENCHMARK FOR LEARNING WITH ARTIFICIAL OBJECTS FOR IMPROVED AERIAL VEHICLE DETECTION
Immanuel Weber, Jens Bongartz, University of Applied Sciences Koblenz, Germany; Ribana Roscher, University of Bonn, Germany
TH1.O-12.5: RSVQA MEETS BIGEARTHNET: A NEW, LARGE-SCALE, VISUAL QUESTION ANSWERING DATASET FOR REMOTE SENSING
Sylvain Lobry, Université de Paris, France; Begüm Demir, Technische Universität Berlin, Germany; Devis Tuia, École Polytechnique Fédérale de Lausanne, Switzerland
TH1.O-12.6: PREDICTING 1-H DEAD FUEL MOISTURE CONTENT AT REGIONAL SCALES USING MACHINE LEARNING FROM HIMAWARI-8 DATA
Chunquan Fan, Binbin He, University of Electronic Science and Technology of China, China; Peng Kong, Hao Xu, Qiang Zhang, Institute of Spacecraft System Engineering (ISSE), China; Xingwen Quan, University of Electronic Science and Technology of China, China

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