This paper presents a methodological approach to estimation of urban population using the volume of single houses and high-rise residential buildings obtained from an IKONOS-2 ortho-image and light detection and raging (lidar) data. The estimates are directly executed at the finest scale level (i.e. the housing unit) and are then aggregated at the census district level for further validation with the aid of official data supplied by the local and federal governments. Unlike prior works, this study executes a thorough assessment of horizontal and elevation accuracy for the IKONOS-2 and lidar data used in the experiment. The methodological stages are threefold: the construction of a 3D city model, the accuracy assessment of the ortho-image and digital surface models (DSMs), and the quantification of urban population. The validation was accomplished by means of linear regression and associated hypothesis tests, considering the estimated population and the reference data. The results showed that there was a systematic underestimation of population. On average, the conducted estimates assessed 31 fewer inhabitants per district and lie 1.35% below the expected values given by the reference data. In spite of the observed underestimation, the estimated population can be regarded as equivalent to the population provided by the reference data at a 1% level of significance. ARTICLE HISTORY

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This paper is committed to explore object-oriented methods for the classification of Quickbird images, aiming to support future urban population estimates. The study area concerns the southern sector of São José dos Campos city, located in the State of São Paulo, Brazil. By means of a multi-resolution segmentation approach and a six-layer hierarchical classification network, homogeneous residential areas were identified in terms of density of occupation and building standards (single dwelling units or high-rise buildings). The classification network was built upon spectral, geometrical and topological features of the objects in each level of segmentation as well as upon their contextual and semantic interrelationships in-between the hierarchical levels. The final classification of homogeneous residential units was subject to validation, using an object-based Kappa statistics.

Este estudo tem como objetivo o desenvolvimento de uma metodologia de classificação automática de áreas urbanizadas contínuas e dispersas que seja replicável em diferentes regiões do Brasil. Com essa metodologia busca-se o aumento da exatidão do mapeamento bem como reduzir a subjetividade e o tempo empregado no procedimento. Para este fim, aplicou-se, usando o software Definiens, a classificação baseada em Objeto em imagem LANDSAT da região de Piracicaba, Limeira e Rio Claro, do estado de São Paulo, obtida em 2007. Este procedimento consiste na segmentação multiresolução das imagens e na classificação baseada na lógica fuzzy. Na avaliação dos resultados foram utilizadas imagens de alta resolução, disponíveis no Google Earth. O bom desempenho obtido na classificação automática da área de estudo (índice global de 0,94 e Kappa de 0,72) indica a viabilidade do método aplicado para outras áreas urbanizadas.