e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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:

Discover all records
Home page


Detection of ancient settlement mounds: Archaeological survey based on the SRTM terrain model


In the present study we demonstrate the value of the SRTM three arcsecond terrain model for a virtual survey of archaeological sites: the detection and mapping of ancient settlement mounds in the Near East. These so-called "tells" are the result of millennia of occupation within the period from 8000-1000 BC, and form visible landmarks of the world's first farming and urban communities. The SRTM model provides for the first time an opportunity to scan areas not yet surveyed archaeologically on a supra-regional scale and to pinpoint probable tell sites. In order to map these historic monuments for the purpose of settlement-study and conservation, we develop a machine learning classifier which identifies probable tell sites from the terrain model. In a test, point-like elevations of a characteristic tell shape, standing out for more than 5 to 6 m in the DEM were successfully detected (851133 tells). False positives (327/(600 1200) pixels) were primarily due to natural elevations, resembling tells in height and size.

  • DE
  • US
  • GB
  • Harvard_Univ (US)
  • Univ_Sheffield (UK)
  • Heidelberg_Univ (DE)
Data keywords
  • machine learning
Agriculture keywords
  • farming
Data topic
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
    Powered by Lodex 8.20.3
    logo commission europeenne
    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.