Earth Observation (EO) is an ever-growing field of investigation where computer vision, machine learning, and signal/image processing meet. The general objective is to provide large-scale, homogeneous information about processes occurring at the surface of the Earth exploiting data collected by airborne and spaceborne sensors. Earth Observation implies the need for multiple inference tasks, ranging from detection to registration, data mining, multi-sensor, multi-resolution, multi-temporal, and multi-modality fusion, and regression, to name just a few. It comprises ample applications like location-based services, online mapping services, large-scale surveillance, 3D urban modelling, navigation systems, natural hazard forecast and response, climate change monitoring, virtual habitat modelling, etc. The sheer amount of data calls for highly automated scene interpretation workflows.

This workshop, held for its fifth edition at the CVPR 2021, aims at fostering collaboration between the computer vision and machine learning disciplines and the remote sensing community to boost automated interpretation of EO data. EarthVision will additionally help build cooperation within the CVPR community for this highly challenging and quickly evolving field that has a big impact on human society, economy, industry, and the planet.

Submissions are invited from all areas of computer vision and image analysis relevant for, or applied to, environmental remote sensing. Topics of interest include, but are not limited to:

• Super-resolution in the spectral and spatial domain
• Hyperspectral and multispectral image processing
• 3D reconstruction from aerial images
• Feature extraction and learning
• Semantic classification of UAV / aerial and satellite images and videos
• Deep learning tailored for Earth observation
• Domain adaptation and concept drift
• Human-in-the-loop
• Multi-resolution, multi-temporal, multi-sensor, multi-modal processing
• Earth observation and machine learning approaches for adaptation to climate change
• Vision and machine learning methods for recovery from disasters and extreme events
• Public benchmark data sets: 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 CVPR2021 workshop proceedings (published open access on the Computer Vision Foundation website) and submitted to IEEE for publication in IEEE Xplore. Publication in IEEE Xplore will be granted only if the paper meets IEEE publication policies and procedures.

The Call for Papers is available here.