Fabio Pacifici, the Chair of the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS) emailed the following announcement to encourage the OpenTopography community to participate in the 2012 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. More information:
2012 IEEE GRSS Data Fusion Contest: This year the Contest is designed to investigate the potential of multi-modal/multi-temporal fusion of very high spatial resolution imagery. Three data sets of three different types (optical, SAR, and LIDAR) over downtown San Francisco are made freely available by DigitalGlobe, Astrium Services, and USGS. They will include very high spatial resolution QuickBird, WorldView-2, TerraSAR-X, and LIDAR imagery. Optical and SAR data sets will be composed of a total of eight images from two acquisition times in 2007 and 2011.
To enter the contest, participants are required to submit a manuscript on a research topic of their own choosing. Papers should describe in detail the problem addressed, the method used, and the final result. Deadline: May 1, 2012.
The winning teams will be eligible to win up to $800 and an open access publication on an IEEE GRSS Journal ($3,000 value). More than 600 users from universities and corporations across the globe have registered in just over a month.
Here the link to the Contest web-page: http://www.grss-ieee.org/community/technical-committees/data-fusion/data... where more information is available.
The Data Fusion Contest is annually proposed since 2006, and it is organized by the Data Fusion Technical Committee of the Geoscience and Remote Sensing Society. The Committee serves as a global, multi-disciplinary, network for geospatial data fusion, connecting people and resources. It aims at educating students and professionals, and at promoting best practices in data fusion applications.The Contest is open not only to IEEE members, but to everyone, with the aim of evaluating existing methodologies at the research or operational level to solve remote sensing problems using data from different sensors.