Machine learning in urban land cover cartographies. A tool for territorial management
DOI:
https://doi.org/10.30972/crn.39397898Keywords:
urban land cover map, automatic learning model, multivariate analysis, geographic information systemsAbstract
This study develops the analysis and semi-automated methodologies used for describing and mapping urban land cover and land uses in the province of Córdoba. The results demonstrate a notable correlation between the resulting classification and the territorial reality it represents. While not definitive, it approximates the characteristics of the built environment and the uses and activities that occur within it. The application of these methodologies is viewed as contributing to key temporal monitoring, including the quantification of the impacts of urbanization, earth surface temperature, the decrease in green areas, and the effects of various land uses in the city, among others. Furthermore, they enable prospective modeling exercises, establishing possible occupancy scenarios aimed at implementing policies for efficient land development and management.
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