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.