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/