AIMS AND SCOPE
Earth Observation (EO)/Remote Sensing is an ever-growing field of investigation where computer vision, machine learning, and signal/image processing meet. The general objective of the domain is to provide large-scale, homogeneous information about processes occurring at the surface of the Earth exploiting data collected by airborne and spaceborne sensors. Earth Observation covers a broad range of tasks, ranging from detection to registration, data mining, multi-sensor, multi-resolution, multi-temporal, and multi-modality fusion, and regression, to name just a few. 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, etc. The sheer amount of data calls for highly automated scene interpretation workflows.

Earth 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. 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. It also connects to other immediate societal challenges such as monitoring of forest fires and other natural hazards, urban growth, deforestation, and climate change.

This workshop, held for its sixth edition at the CVPR 2022, aims at fostering collaboration between the computer vision and EO communities to, on the one hand, boost automated interpretation of EO data, and, on the other hand, raise awareness inside the computer vision and machine learning communities for this highly challenging and quickly evolving field of research with an extensive impact on human society, economy, industry, and the environment.

Submissions are invited from all areas of computer vision and image analysis relevant for, or applied to, environmental remote sensing. Topics of interest include, but are not limited to:
– Super-resolution in the spectral and spatial domain
– Hyperspectral and multispectral image processing
– 3D reconstruction from aerial optical and LiDAR acquisitions
– Feature extraction and learning from spatio-temporal data
– Semantic classification of UAV / aerial and satellite images and videos
– Deep learning tailored for large-scale Earth observation
– Domain adaptation, concept drift, and the detection of out-of-distribution data
– Self-, weakly, and unsupervised approaches for learning with spatial data
– Human-in-the-loop and active learning
– Multi-resolution, multi-temporal, multi-sensor, multi-modal processing
– Fusion of machine learning and physical models
– Explainable and interpretable machine learning in Earth Observation applications
– Applications for climate change, sustainable development goals, and geoscience
– Public benchmark datasets: Training data standards, testing & evaluation metrics, as well as open-source research and development.

IMPORTANT DATES
Full paper submission: March 9, 2022
Notification of acceptance: April 1, 2022
Camera-ready paper: April 8, 2022
Workshop (full day): June 19, 2022

SUBMISSION GUIDELINES
A complete paper should be submitted using the EarthVision templates provided on the workshop website. The paper length must not exceed 8 pages (excluding references) and formatting follows CVPR 2022 instructions. All manuscripts will be subject to a double-blind review process, i.e. authors must not identify themselves on the submitted papers.

Papers are to be submitted using the dedicated submission platform on the Workshop website (https://www.grss-ieee.org/earthvision2022/submission.html). By submitting a manuscript, the authors guarantee that it has not been previously published or accepted for publication in a substantially similar form. CVPR rules regarding plagiarism, double submission, etc. apply.

WORKSHOP ORGANIZERS
– Ronny Hänsch, German Aerospace Center, Germany
– Devis Tuia, EPFL, Switzerland
– Jan Dirk Wegner, University of Zurich & ETH Zurich, Switzerland
– Bertrand Le Saux, ESA/ESRIN, Italy
– Naoto Yokoya, Uni. of Tokyo & RIKEN, Japan
– Nathan Jacobs, Uni. of Kentucky, USA
– Fabio Pacifici, Maxar, USA
– Mariko Burgin, NASA JPL, USA
– Loïc Landrieu, IGN, France
– Charlotte Pelletier, UBS Vannes, France

CHALLENGE
EarthVision 2022 will again feature interesting challenges addressing modern problems of Remote Sensing and Earth Observation. Stay tuned for details!

SPONSORING
The event is co-organized by the Image Analysis and Data Fusion Technical Committee of the IEEE-GRSS, and it is sponsored by SpaceNet.