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
Under the constant advancement of remote sensing technology, more and more high spatial resolution data are becoming available, the developments not only greatly increased our capability and accuracy in information extraction but also opened new application opportunities. Existing research activities have focused on how to increase the utilization efficiency of high resolution data, particularly in a wide range of applications such as urban environment, urban-planning, precision agriculture, forest inventory and risk assessment,however,the overall level of automation is still low. The paper adopted the multi-feature information of high-resolution images, objected-oriented image analysis approaches and multi-scale image segmentation technology, established a corresponding relevant knowledge base to extract information. Also the results show the new approach provided a satisfying solution to improve the level of autoimmunization and intelligentization of high-resolution remote sensing data process and application
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