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Using Remote Data Mining on LIDAR and Imagery Fusion Data to Develop Land Cover Maps


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dc.creator García Gutiérrez, Jorge es
dc.creator Martínez Álvarez, Francisco es
dc.creator Riquelme Santos, José Cristóbal es 2016-04-27T10:04:02Z 2016-04-27T10:04:02Z 2010
dc.description.abstract Remote sensing based on imagery has traditionally been the main tool used to extract land uses and land cover (LULC) maps. However, more powerful tools are needed in order to fulfill organizations requirements. Thus, this work explores the joint use of orthophotography and LIDAR with the application of intelligent techniques for rapid and efficient LULC map generation. In particular, five types of LULC have been studied for a northern area in Spain, extracting 63 features. Subsequently, a comparison of two well-known supervised learning algorithms is performed, showing that C4.5 substantially outperforms a classical remote sensing classifier (PCA combined with Naive Bayes). This fact has also been tested by means of the non-parametric Wilcoxon statistical test. Finally, the C4.5 is applied to construct a model which, with a resolution of 1 m 2, obtained precisions between 81% and 93%. es
dc.format application/pdf es
dc.language.iso eng es
dc.relation.ispartof Trends in Applied Intelligent Systems, Lecture Notes in Computer Science, Volume 6096, pp 378-387 es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Data mining es
dc.subject Remote sensing es
dc.subject LIDAR es
dc.subject Imagery es
dc.subject LULC es
dc.title Using Remote Data Mining on LIDAR and Imagery Fusion Data to Develop Land Cover Maps es
dc.type info:eu-repo/semantics/bookPart es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.identifier.doi es
idus.format.extent 9 es
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