The analysis of informal settlements in the Brazilian city of Campo Grande aimed to identify, categorize and understand the growth of slum areas to inform housing policies. The study employed both avant-garde technologies and established methods to achieve its results.
The research started with a morphological analysis of informal settlements using satellite images, orthophotos, and the city’s plot map. These databases allowed for the categorization of this type of housing, observing its distribution in the territory, both in its context and in comparison with the rest of the city. This morphological categorization provided the foundation for training algorithms to automatically identify informal settlements using cutting-edge deep-learning techniques developed by the Inter-American Development Bank.
By combining the results of deep learning techniques and the morphological study, an atlas of informal settlements in Campo Grande was created, providing a comprehensive understanding of the problem in the city. In addition to informing local housing policies, the Atlas of Informal Settlements places Campo Grande at the forefront of studies on informal cities in the Latin American context.