Call for Papers

Call for Paper: EarthVision at CVPR 2025 - Large Scale Computer Vision for Remote Sensing Imagery
https://www.grss-ieee.org/events/earthvision-2025/

June 11/12th, Nashville, TN, USA

Accepted papers will be published in the CVPR Workshop Proceedings.

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.

The full-day workshop will provide a forum for presenting original
research in computer vision and pattern recognition applied to
large-scale remote sensing imagery. The focus will be on recent
advancements in automatic analysis of remote sensing imagery for Earth
Observation and its impact on geoscience, climate change, sustainable
development goals, and the general understanding of the Earth
system. 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 spatiotemporal 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.

Important Dates
- Submission deadline: March 3, 2025
- Notification to authors: March 31,  2025 
- Camera-ready deadline: April 7, 2025 
- Workshop: June 11/12, 2025