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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Title

TWO-STAGE SUBPIXEL IMPERVIOUS SURFACE COVERAGE ESTIMATION: COMPARING C 5.0/CUBIST AND RANDOM FOREST

en
Abstract

The paper presents accuracy comparison of subpixel classification based on medium resolution satellite images (Landsat 5TM), performed using two machine learning algorithms (C5.0/Cubist and Random Forest) built on decision and regression trees method. The research was conducted for the immediate catchment of the Dobczyce Reservoir (which is the main source of water supply for the city of Cracow, Poland) along with an adjacent area of towns (Myslenice and Dobczyce). The land use in the catchment is dominated by agriculture with numerous villages of dispersed development. The southern part of the study area is covered mainly by forests. The aim of the study was to obtain image of percentage impervious surface coverage, valid for the period of 2009-2010. Imperviousness index map generation was a two-stage procedure based on two algorithms. The first step was hard classification, performed using decision trees method, which divided the study area into two categories: i) completely permeable (imperviousness index less than 1%) and ii) fully or partially impervious areas. In a second stage, for pixels classified as impervious, the percentage of impervious surface coverage in pixel area was estimated using regression trees approach. Accuracy of the final imperviousness index map was checked based on validation data set, which was not used for learning and testing of classifiers. The root mean square error (RMS) of determination of the percentage of the impervious surfaces within a single pixel was +/-12.8% for C5.0/Cubist method and +/-13.9% in case of Random Forest method. Further results analysis shown, that in intensively urbanized areas small imperviousness index value differences occurred. Larger differences of up to a few percent were found in agriculture and forests areas, with more accurate results obtained using C5.0/Cubist method.

en
Year
2014
en
Country
  • PL
Organization
  • AGH_Univ_Sci_&_Technol (PL)
Data keywords
  • machine learning
en
Agriculture keywords
  • agriculture
en
Data topic
  • modeling
  • sensors
en
SO
GEOCONFERENCE ON INFORMATICS, GEOINFORMATICS AND REMOTE SENSING, VOL III
Document type

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Institutions 10 co-publis
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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.