Earth Observation (EO)/Remote Sensing (RS) 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, 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 shear amount of data needs highly automated workflows.
This workshop, held at the CVPR 2017 conference http://cvpr2017.thecvf.com/, aims at fostering collaboration between the computer vision and Earth Observation communities to boost automated interpretation of EO data and to raise awareness inside the vision community for this highly challenging and quickly evolving field of research with a big impact on human society, economy, industry, and the planet.
The event is jointly organized by the Image Analysis and Data Fusion Technical Committee http://www.grss-ieee.org/community/technical-committees/data-fusion/ of the IEEE-GRSS http://www.grss-ieee.org/ and by the ISPRS Commission II "Photogrammetry" http://www2.isprs.org/commissions/comm2.html and is sponsored by IEEE-GRSS.
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 ultra-spectral image processing
- 3D reconstruction from aerial and satellite 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
- Multi-resolution, multi-temporal, multi-sensor, multi-modal processing
- Public benchmark data sets: training data standards, testing & evaluation metrics, and open source research and development.
All manuscripts will be subject to a double-blind review process. Accepted EARTHVISION papers will be included in the USB proceedings of CVPR and submitted to IEEE for possible publication in IEEE Xplore. Publication in IEEE Xplore will be granted only if the paper meets IEEE publicaiton policies and procedures.