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.

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Complexity metrics to quantify semantic accuracy in segmented Landsat images


This paper addresses semantic accuracy in relation to images obtained with remote sensing. Semantic accuracy is defined in terms of map complexity. Complexity metrics are applied as a metric to measure complexity. The idea is that a homogeneous map of a low complexity is of a high semantic accuracy. In this study, complexity metrics like aggregation index, fragmentation index and patch size are applied on two images with different objectives, one from an agricultural area in the Netherlands, and one from a rural area in Kazakhstan. Images are segmented first using region merging segmentation. Effects on metrics and semantic accuracy are discussed. On the basis of well-defined subsets, we conclude that the complexity metrics are suitable to quantify the semantic accuracy of the map. Segmentation is the most useful for an agricultural area including various agricultural fields. Metrics are mutually comparable being highly correlated, but showing some different aspects in quantifying map homogeneity and identifying objects of a high semantic accuracy.

  • NL
  • US
  • Univ_Nebraska_Lincoln_UNL (US)
  • Univ_Twente (NL)
Data keywords
  • semantic
Agriculture keywords
  • agriculture
Data topic
  • semantics
  • sensors
Document type

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

Institutions 10 co-publis
  • Univ_Nebraska_Lincoln_UNL (US)
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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.