The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Modern imaging sensors, especially those aboard satellites, continuously deliver enormous amounts of data. The widespread of meter resolution images, is not only exploding the volumes of acquired data but also brings a new dimension in the image detail, thus growing the information content. These represent typical cases, where users need automated tools to discover, explore and explain the contents of large image databases. There is a strong need to build up applications that help the user in image interpretation task, applications that permit to query the archives in content based mode, without having to know all the information contained in the images at signal level. We propose in this article, a synergy between stochastic modelling, knowledge discovery, and semantic representation. To do that, we associate semantic labels to a combination of primitive image features. The user-defined semantic image content interpretation is linked with Bayesian networks to a completely unsupervised classification. This new paradigm for the interaction with EO archives can provide several applications for users coming from different domains, as change detection, agricultural field classification, environment monitoring, atmosphere effects or urbanization.
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