EARTHVISION 2025
June 11/12th, Nashville, TN, USA
in conjuction with the Computer Vision and Pattern Recognition (CVPR) 2025 Conference
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 ( https://sdgs.un.org/goals ). 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
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 processing
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 development.
All manuscripts will be subject to a double-blind review process. Accepted EarthVision papers will be included in the CVPR2024 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
All deadlines are considered end of day anywhere on Earth.
| March 3, 2025 | Submission deadline | |
| March 31, 2025 | Notification to authors | |
| April 7, 2025 | Camera-ready deadline | |
| June 11/12, 2025 | Workshop |
Organizers
- Ronny Hänsch, German Aerospace Center, Germany,
- Devis Tuia, EPFL, Switzerland,
- Jan Dirk Wegner, University of Zurich & ETH Zurich, Switzerland,
- Nathan Jacobs, Washington University in St. Louis, USA
- Loïc Landrieu, ENPC ParisTech, France
- Charlotte Pelletier, UBS Vannes, France
- Hannah Kerner, Arizona State University, USA
Technical Committee
TBA
Sponsors
Affiliations
Submissions
TBA
Program
TBA
CVPR 2025
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.
