![]() ![]() ![]() Marc Rußwurm, École Polytechnique Fédérale de Lausanne.Luc Baudoux, National French mapping agency (IGN).Kuldeep Kurte, Oak Ridge National Laboratory.Jonathan Sullivan, University of Arizona.Jonathan Giezendanner, University of Arizona.Jacob Arndt, Oak Ridge National Laboratory.Helmut Mayer, Bundeswehr University Munich.Gustau Camps-Valls, Universitat de València.Gülşen Taşkın, İstanbul Teknik Üniversitesi.Franz Rottensteiner, Leibniz Universitat Hannover, Germany.Elliot Vincent, École des ponts ParisTech / Inria.Dimitri Gominski, University of Copenhagen.Dalton Lunga, Oak Ridge National Laboratory.Christian Heipke, Leibniz Universität Hannover.Amanda Bright, National Geospatial-Intelligence Agency.Beth Tellman, University of Arizona, USA.Hannah Kerner, Arizona State University, USA.Charlotte Pelletier, UBS Vannes, France.Nathan Jacobs, Washington University in St.Louis, USA.Jan Dirk Wegner, University of Zurich & ETH Zurich, Switzerland,.Ronny Hänsch, German Aerospace Center, Germany,. ![]() Publication in IEEEXplore will be granted only if the paper meets IEEE publication policies and procedures. Accepted EarthVision papers will be included in the CVPR2023 workshop proceedings (published open access on the Computer Vision Foundation website) and submitted to IEEE for publication in IEEEXplore. Public benchmark datasets: training data standards, testing & evaluation metrics, as well as open source research and development.Īll manuscripts will be subject to a double-blind review process. Multi-resolution, multi-temporal, multi-sensor, multi-modal processingįusion of machine learning and physical modelsĮxplainable and interpretable machine learning in Earth Observation applicationsĪpplications for climate change, sustainable development goals, and geoscience Self-, weakly, and unsupervised approaches for learning with spatial data Reconstruction and segmentation of optical and LiDAR 3D point cloudsįeature extraction and learning from spatio-temporal dataĪnalysis of UAV / aerial and satellite images and videosĭeep learning tailored for large-scale Earth Observationĭomain adaptation, concept drift, and the detection of out-of-distribution data Hyperspectral and multispectral image processing Super-resolution in the spectral and spatial domain It also connects to other immediate societal challenges such as monitoring of forest fires and other natural hazards, urban growth, deforestation, and climate change.Ī non exhaustive list of topics of interest includes the following: The aim of EarthVision to advance the state of the art in machine learning-based analysis of remote sensing data is thus of high relevance. The sheer amount of data calls for highly automated scene interpretation workflows.Įarth Observation and in particular the analysis of spaceborne data directly connects to 34 indicators out of 40 (29 targets and 11 goals) of the Sustainable Development Goals defined by the United Nations ( ). It is motivated by numerous applications such as location-based services, online mapping services, large-scale surveillance, 3D urban modeling, navigation systems, natural hazard forecast and response, climate change monitoring, virtual habitat modeling, food security, etc. Earth Observation covers a broad range of tasks, from detection to registration, data mining, and multi-sensor, multi-resolution, multi-temporal, and multi-modality fusion and regression, to name just a few. The general objective of the domain is to provide large-scale and consistent information about processes occurring at the surface of the Earth by exploiting data collected by airborne and spaceborne sensors. Earth Observation (EO) and remote sensing are ever-growing fields of investigation where computer vision, machine learning, and signal/image processing meet. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |