BARROS, D. F.; BARROS, Débora Ferreira de.
Resumen:
Due to population growth and the expansion of informal settlements, it is necessary to monitor and
map these places so that public policies can be developed aimed at solving the precarious nature
present in these spaces. Some of the current solutions involve image classification based on machine
learning algorithms, however, those present in the state of the art require the extraction of many
features, which takes a lot of time and generates a large amount of parameters that need to be
processed by the algorithms. This work presents the use of a Convolutional Neural Network, a U-Net
with Inception ResNet-V2, as a solution for the feature extraction automation and parameter
reduction in satellite images, focusing on the city of João Pessoa, in Paraíba, together with the
segmentation of the images aiming at the detection and classification of precarious settlements in
urban spaces. The model was evaluated using the Jaccard and Dice coefficients, which presented
respectively 53% and 69%, in the test dataset.