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<div class="pre" style="margin: 0; padding: 0; font-family: monospace">Dear VisionList,<br /> <br /> CALL FOR PARTICIPANTS & PAPERS <br /> <br /> EarthVision 2020 - Large Scale Computer Vision for Remote Sensing Imagery Workshop in conjunction with CVPR 2020, 14th June 2020, Seattle, USA. </div>
<div class="pre" style="margin: 0; padding: 0; font-family: monospace">Website: <a href="https://www.grss-ieee.org/earthvision2020/" rel="noreferrer">https://www.grss-ieee.org/earthvision2020/</a></div>
<div class="pre" style="margin: 0; padding: 0; font-family: monospace"><br /> Earth Observation (EO) and remote sensing are fastly-growing fields of research where computer vision, machine learning, and signal/image processing meet. This workshop, held for its fourth edition at the CVPR 2020, aims at fostering collaboration between the computer vision as well as machine learning disciplines and the remote sensing community to boost automated interpretation of EO data. <br /> <br /> We invite contributions in the fields of (not exhaustive list): <br /> - Super-resolution in the spectral and spatial domain <br /> - Hyperspectral and multispectral image processing <br /> - 3D reconstruction from aerial images <br /> - Feature extraction and learning <br /> - Semantic classification of UAV / aerial and satellite images and videos <br /> - Deep learning tailored for Earth observation <br /> - Domain adaptation and concept drift <br /> - Human-in-the-loop <br /> - Multi-resolution, multi-temporal, multi-sensor, multi-modal processing <br /> - Public benchmark data sets: Training data standards, testing & evaluation metrics, as well as open source research and development.<br /> <br /> CHALLENGE<br /> Spanning three years while featuring five unique datasets and challenges, SpaceNet (<a href="https://spacenet.ai/" rel="noreferrer">https://spacenet.ai/</a>) has continued to focus on different aspects of applying machine learning to solve difficult foundational mapping problems. Looking ahead to the EarthVision 2020 challenge, SpaceNet 6 will take us to a new frontier of data modality: Synthetic Aperture Radar (SAR). For the SpaceNet 6 challenge, participants will be asked to automatically extract building footprints with computer vision and artificial intelligence algorithms using SAR data.<br /> <br /> IMPORTANT DATES <br /> Full paper submission: March 7, 2020 <br /> Notification of acceptance: March 28, 2020 <br /> Camera-ready paper: April 10, 2020 <br /> Workshop (full day): June 14, 2020<br /> <br /> WORKSHOP ORGANIZERS<br /> - Devis Tuia, Wageningen University, the Netherlands<br /> - Jan Dirk Wegner, ETH Zurich, Switzerland<br /> - Ronny Hänsch, German Aerospace Center, Germany<br /> - Bertrand Le Saux, ONERA, France<br /> - Naoto Yokoya, RIKEN, Japan<br /> - Ilke Demir, DeepScale<br /> - Nathan Jacobs, Uni. of Kentucky<br /> - Krzysztof Koperski, Maxar<br /> - Fabio Pacifici, Maxar<br /> - Ramesh Raskar, MIT<br /> - Myron Brown, John Hopkins University<br /> - Mariko Burgin, NASA JPL<br /> <br /> Full CfP: <a class="c-link" href="https://slack-redir.net/link?url=http%3A%2F%2Ftiny.cc%2Fxtpgiz&v=3" rel="noopener noreferrer">http://tiny.cc/xtpgiz</a><br /> <br /> See you soon! <br /> <br />
<blockquote type="cite" style="padding: 0 0.4em; border-left: #1010ff 2px solid; margin: 0">From the organizing team,</blockquote>
<br /> Bertrand Le Saux</div>
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