Automatic detection of deprived urban areas using Google Earth™ images of cities from the Brazilian semi-arid region

Abstract

Automatic classification of deprived urban areas provides vital information for implementing pro-poor policies. In this paper, an approach for the classification of these areas in Brazilian cities is presented. Satellite images were obtained free of charge from six cities in the Brazilian semi-arid region using Google Earth Engine software. To assess the discriminative power of census data, data made publicly available by Brazilian Institute of Geography and Statistics (IBGE) were used to train SVM classifiers together with features extracted from images. The image features were extracted using the following approaches: color histograms, LBP histograms, and lacunarity. Four evaluation tests were investigated based on two criteria: use of census data and cross-validation method. Two types of cross-validation were used: 10-fold and leave-one-city-out. The use of census data caused a negative impact on the results. This impact is justified by the criteria on which census tracts are mapped in the country, not only morphological and visually perceptible through satellite images, as opposed to adopted extraction approaches. The best results obtained were average accuracy of 91.81% and average F1-score of 92.27%. This research contributes to the recognition of deprived urban areas and urban socio-spatial dynamics, supporting urban-territorial planning.

Development of a two-stage method for segmenting the color images of urban terrain acquired from space optic-electronic observation systems based on the ant algorithm and the hough algorithm

“…In [20], for the automatic classification of urban areas in the images, it is proposed to use classifier training simultaneously with the features obtained from the satellite image database. The advantage of [20] is the consideration of color histograms to obtain the features of the image of urban areas. The disadvantage of [20] is the identification of only large planar objects such as an urban area, without objects of interest in the area.…”

“…The advantage of [20] is the consideration of color histograms to obtain the features of the image of urban areas. The disadvantage of [20] is the identification of only large planar objects such as an urban area, without objects of interest in the area.…”