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
With the advent of Sensor Web, the satellite data acquired by sensor systems could be shared among users immediately. Our research has led to an implementation of natural language queries such that users without particular knowledge of satellite imagery can describe easily for what they need. We use a rules-based method to retrieve named entities, with the help of a knowledge base and uses existing Sensor Web services for acquiring stored or real time satellite data. We use rule-based methods to align time, location and domain task entities in natural language queries with Sensor Web services with standard times, geographical coordinates, and satellite attributes. To evaluate our system, we wrote a series of natural language queries in the domains of surveying and mapping, forestry, agriculture, and disaster response. Our queries and satellite data retrieved by the queries were corrected by a group of experts to create a gold standard. Using their remarks as correct, we scored our system results using precision and recall metrics standard for information retrieval. The results of our experiment demonstrate that the proposed method is promising for assisting in Earth observation applications.
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