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
Knowledge-Based Object Oriented Land Cover Classification Using SPOT5 Imagery in Forest-Agriculture Ecotones
This paper describes a knowledge-based object oriented classification method using SPOT5 imagery in Forest-Agriculture Ecotones. It is based on optimized application of expert knowledge information extraction from remote sensing imagery, geographic data, investigated data, chessboard image segmentation and multi-resolution image segmentation technique. Due to these capabilities, the method represents a significant improvement in land cover classification. This approach can also be seen as a framework for integrating external knowledge with image classification procedures. Confusion matrix is used to do accuracy assessment and our assessment results show that he knowledge-based object oriented classification improves the total accuracy from 61.352% (pixel-based Minimum Distance classification), 91.30% (object oriented Nearest Neighbor classification) to 94.40%. The result indicates that this method leads to a higher classification accuracy.
Inappropriate format for Document type, expected simple value but got array, please use list format