@InProceedings{CarvalhoKuxFlorSilv:2015:GeObIm,
author = "Carvalho, Marcus and Kux, Hermann Johann Heinrich and Florenzano,
Teresa Galotti and Silva, Gabriella",
affiliation = "{Universidade Federal Fluminense (UFF)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Universidade Federal do Rio de Janeiro
(UFRJ)}",
title = "Geographic object-based image analysis (GEOBIA) and data mining
for urban land use classification by blocks using WorldView-2
images",
year = "2015",
organization = "International Cartographic Conference, 27.",
keywords = "Geographic object-based image analysis, Data mining, Urban land
use by blocks.",
abstract = "The objective of this study is to develop and evaluate a
methodology for the analysis of WorldView-2 images based on
Geographic Object-Based Image Analysis (GEOBIA) and Data Mining,
to classify urban land use per block. The area under study is a
section at the western part of S{\~a}o Paulo Metropolitan Region.
Mapping land use per urban block is an important information
source for managers and decision makers in urban areas. Among the
land cover classes considered in this work, seven were used by the
S{\~a}o Paulo Municipality in the official maps. Objects located
within the blocks are helpful to characterize these areas. So, in
order to analyze the context and the relationship among classes
for the elaboration of land use mapping per block, a
classification procedure was adopted previously done with good
accuracy considering a lower hierarchical level (sub-objects) at
the level of blocks (super-objects). The steps followed were:
selection and sample collection at the blocks to train the
classifier, choice of attributes to be analyzed by the data mining
algorithm, generation and implementation of a decision tree using
the DEFINIENS Developer software, for the classification of the
WorldView-2 image. It is concluded that the use of the OBIA
paradigm and Data Mining techniques were helpful for mapping urban
land use. The Kappa index was 0.7050 and the global precision
0.7556.",
conference-location = "Rio de Janeiro, RJ",
conference-year = "23-28 Aug.",
urlaccessdate = "20 abr. 2024"
}