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Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part B: 3-D Contest

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Vo A. V., Truong-Hong L., Laefer D. F., Tiede D., Doleire-Oltmanns S., Baraldi A., Shimoni M., Moser G., Tuia D.,
Project Multimodal machine learning for remote sensing information fusion
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Original article (peer-reviewed)

Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume (Issue) 9(12)
Page(s) 5560 - 5575
Title of proceedings IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
DOI 10.1109/jstars.2016.2581843

Open Access

URL http://ieeexplore.ieee.org/document/7542155/
Type of Open Access Publisher (Gold Open Access)

Abstract

© 2008-2012 IEEE. In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high resolution RGB images (with a ground sample distance of 5 cm) and a 3-D light detection and ranging point cloud (with a point cloud density of approximatively 65 pts/m 2 ). The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this Part B, we report the results obtained by the winners of the 3-D contest, which explored challenging tasks of road extraction and ISO containers identification, respectively. The 2-D part of the contest and a detailed presentation of the dataset are discussed in Part A.
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