Large Scale Computer Vision for Remote Sensing Imagery Workshop Call for Papers
CALL FOR PARTICIPANTS & PAPERS
EarthVision 2023 -
Large Scale Computer Vision for Remote Sensing Imagery Workshop
in conjunction with CVPR 2023, June 2023, Vancouver, Canada.
Website: https://www.grss-ieee.org/earthvision2023/
AIMS AND SCOPE
Earth Observation (EO) and remote sensing are fast growing fields of
investigation where computer vision, machine learning, and
signal/image processing meet. The general objective of EO 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. EO covers a broad range of tasks,
from detection to registration, data mining, and multi-sensor,
multi-resolution, multi-temporal, multi-modal fusion and regression,
to name just a few. It serves numerous applications such as
location-based services, online mapping, large-scale surveillance, 3D
urban modeling, navigation systems, natural hazard forecast and
response, climate change monitoring, virtual habitat modeling, food
security, etc. The sheer amount of data calls for highly automated
scene interpretation workflows.
The Earthvision workshop, held for its seventh edition at the CVPR
2023, aims at fostering collaboration between the computer vision,
machine learning, and the remote sensing communities to boost
automated analysis of EO data. EarthVision will strive to build
cooperation within the CVPR community for this highly challenging and
quickly evolving field with a significant impact on society, economy,
industry, and the environment.
We invite contributions in the fields of (not exhaustive list):
Super-resolution in the spectral and spatial domain
Hyperspectral and multispectral image processing
Reconstruction and segmentation of optical and LiDAR 3D point
clouds
Feature extraction and learning from spatio-temporal data
Analysis 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
Evaluating models using unlabeled 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, 2023
Notification of acceptance: March 30, 2023
Camera-ready paper: April 6, 2023
Workshop (full day): June 18, 2023
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 2023
instructions. All manuscripts will be subject to a double-blind review
process, i.e. authors must not identify themselves on the submitted
papers. The reviewing process is single-stage, meaning that there will
not be rebuttals to reviewers.
Papers are to be submitted using the dedicated submission platform on
the workshop website. By submitting a manuscript, the authors
guarantee that it has not been previously published or accepted for
publication in 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
Nathan Jacobs, Washington University in St. Louis, USA
Loïc Landrieu, IGN, France
Charlotte Pelletier, UBS Vannes, France
Hannah Kerner, Arizona State University, USA
Beth Tellman, University of Arizona, USA
CHALLENGE
EarthVision 2023 will feature the African Biomass Challenge with the
goal to accurately estimate aboveground biomass in different cocoa
plantations in Côte d'Ivoire. The dataset consists of ESA
Sentinel-2 images, NASA GEDI data and ground truth biomass. All AI
practitioners, experts and enthusiasts are invited to take part in the
competition organized on Zindi.
SPONSORING
The event is co-organized by the Image Analysis and Data Fusion
Technical Committee of the IEEE-GRSS, and it is sponsored by Blacksky,
Exolabs, Picterra, and Kitware.
Website: https://www.grss-ieee.org/earthvision2023/