June 18th, Vancouver, Canada

Aims and Scope

Earth Observation (EO) and remote sensing are ever-growing fields of investigation where computer vision, machine learning, and signal/image processing meet. 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 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. 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. 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.

A non exhaustive list of topics of interest includes the following:

  • 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.

All manuscripts will be subject to a double-blind review process. 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. Publication in IEEEXplore will be granted only if the paper meets IEEE publication policies and procedures.

Important Dates

March 9, 2023Submission deadline 
March 30, 2023Notification to authors 
April 16, 2023Camera-ready deadline 
June 18/19, 2023Workshop 


  • 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, Univ. of Kentucky, USA
  • Loïc Landrieu, IGN, France
  • Charlotte Pelletier, UBS Vannes, France
  • Hannah Kerner, Arizona State University, USA
  • Beth Tellman, University of Arizona, USA

Technical Committee

  • TBA


Reliable, large-scale biomass estimation is a big challenge for the African continent. If solved accurately and cost-efficiently, it can help develop the entire African continent by enabling use cases like reforestation, sustainable agriculture or green finance. For this reason, the organizers of the African Biomass Challenge (GIZ, BNETD, data354, University of Zurich, ETH Zurich, the University of Queensland) have decided to launch one of the largest African AI and data science competitions, whose ultimate goal is to accurately estimate aboveground biomass on any part of the continent using remote sensing data. For this first version of the challenge, they have put together a dataset consisting of ESA Sentinel-2 images, NASA GEDI data and ground truth biomass collected in different cocoa plantations in Côte d'Ivoire. All AI practitioners and enthusiasts are invited to take part in the competition organized on Zindi.










1. Prepare the anonymous, 8-page (references excluded) submission using the ev2023-template and following the paper guidelines. 

2. Submit at


A complete paper should be submitted using the EarthVision templates provided above.

Reviewing is double blind, i.e. authors do not know the names of the reviewers and reviewers do not know the names of the authors. Please read Section 1.7 of the example paper earthvision.pdf for detailed instructions on how to preserve anonymity. Avoid providing acknowledgments or links that may identify the authors.

Papers are to be submitted using the dedicated submission platform: The submission deadline is strict.

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.



CVPR 2023

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

Learn More: CVPR 2023