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The land issue in Brazil is complex and involves various socio-economic, environmental, and, above all, political aspects. With the specialization of spaces, polarizations and functional hierarchies of territories emerge, which, if poorly managed, may contribute to the perpetuation of a system that propagates inequalities. As the smallest-scale public and territorial administrative unit within the country's political-institutional framework, each Brazilian municipality can be considered a social experiment (political, economic, environmental) in spatial landscape organization. The local dynamics of the composition of various types of land cover can indicate greater proximity to or, conversely, greater distance from the achievement of SDG 11 (Sustainable Cities and Communities) at the municipal level, while the set of these experiments can reveal whether Brazil, in its entirety, is effectively moving toward achieving this goal. Using Machine Learning methods and the conceptual framework of Critical Geography (based on the work of Milton Santos), this study analyzes, at both local and national scales, the mosaic of land use and cover provided by the MapBiomas platform, guided by two key questions: (a) Which types of land use (according to the classification adopted by MapBiomas) have driven other spatial changes (as indicated by the increase or suppression in territorial area, in hectares)? (b) Do the identified spatial changes create mosaics that align with socio-environmental sustainability or reflect neglect toward the 2030 Agenda for Sustainable Development?