Large Scale Computer Vision for Remote Sensing Imagery Workshop Call for Papers


EarthVision 2024 - 
Large Scale Computer Vision for Remote Sensing Imagery Workshop 

in conjunction with CVPR 2024, June 2024, Seattle, USA. 



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

    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

    Data-centric machine learning

    Evaluating models using unlabeled data

    Self-, weakly, and unsupervised approaches for learning with
    spatial data

    Foundation models and representation learning in the context of EO

    Human-in-the-loop and active learning

    Multi-resolution, multi-temporal, multi-sensor, multi-modal

    Fusion of machine learning and physical models

    Explainable and interpretable machine learning in Earth
    Observation applications

    Uncertainty quantification of machine-learning based prediction
    from EO data

    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


Full paper submission: March 8, 2024

Notification of acceptance: April 5, 2024

Camera-ready paper: April 12, 2024

Workshop (full day): June 17/18, 2024


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 2024
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.


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

Loïc Landrieu, IGN, France

Charlotte Pelletier, UBS Vannes, France

Hannah Kerner, Arizona State University, USA


The event is co-organized by the Image Analysis and Data Fusion
Technical Committee of the IEEE-GRSS, and it is sponsored by
Exolabs. If your organization is interested to co-sponsor the event,
please don’t hesitate to reach out.