EARTHVISION 2026
June 3/4, 2026, Denver, CO, USA
in conjuction with the Computer Vision and Pattern Recognition (CVPR) 2026 Conference
Aims and Scope / Call for Papers
EO combines computer vision, machine learning, and signal processing to derive large-scale, consistent information about the Earth surface from airborne and spaceborne sensors. It encompasses a wide range of tasks, including detection, registration, data fusion, and regression across multi-sensor, multi-resolution, and multi-temporal data. Applications span mapping, natural hazards, urban modeling, climate monitoring, and food security. The vast amount of EO data necessitates highly automated scene interpretation workflows. EO directly supports 34 out of 40 UN Sustainable Development Goal indicators, underscoring the relevance of the EarthVision workshop in advancing machine learning-based analysis for societal challenges such as forest fire monitoring, urban growth, deforestation, and climate change.
Automated EO interpretation remains a demanding problem for computer vision due to: (i) complex scene layouts requiring specialized priors; (ii) the diversity of sensors and modalities – such as SAR, LiDAR, and hyperspectral imaging – creating vast, heterogeneous datasets; and (iii) the wide range of spatial resolutions, from centimeters in UAV imagery to kilometers for geostationary satellites. These characteristics make EO data an invaluable test bed complementing standard computer vision benchmarks.
The EarthVision workshop seeks to strengthen collaboration between the EO, computer vision, and machine learning communities, fostering innovation in automated geospatial analysis. By raising awareness of this rapidly evolving and impactful research area, EarthVision promotes the development of scalable, efficient, and trustworthy vision systems that advance environmental understanding and global sustainability.
Given the context of EO, the non-exhaustive list of topics of interest includes:
- Deep learning tailored for large-scale Earth Observation
- Multi-resolution, multi-temporal, multi-sensor, multi-modal processing
- 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 spatiotemporal data
- Analysis of UAV / aerial and satellite images and videos
- Detection, registration, classification, and regression in EO data
- Fusion of machine learning and physical models
- Foundation models and representation learning
- Domain adaptation, concept drift, and the detection of out-of-distribution data
- Self-, weakly, and unsupervised approaches for learning with spatial data
- Data-centric machine learning
- Human-in-the-loop and active learning
- Explainable and interpretable machine learning
- Uncertainty quantification
- Public benchmark datasets, training data standards, evaluation metrics
- Scalable automated EO pipelines
- Fusion of language and Earth observation models
All manuscripts will be subject to a double-blind review process. Accepted EarthVision papers will be included in the CVPR2026 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
Important Dates
All deadlines are considered end of day anywhere on Earth.
| March 2, 2026 | Submission deadline | |
| March 31, 2026 | Notification to authors | |
| April 9, 2026 | Camera-ready deadline | |
| June 3/4, 2026 | Workshop |
Program
TBD
Organizers
- Ronny Hänsch, German Aerospace Center, Germany,
- Devis Tuia, EPFL, Switzerland,
- Jan Dirk Wegner, University of Zurich, Switzerland,
- Loïc Landrieu, IGN, France
- Hannah Kerner, Arizona State University, USA
- Nathan Jacobs, Washington University in St. Louis, USA
Technical Committee
TBD
Affiliations
.
Submissions
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
CVPR 2026
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.
