In a world in which the pace of cities is increasing, prompt access to relevant information is crucial to the understanding and regulation of land use and its evolution in time. In spite of this, characterization and regulation of urban areas remains a complex process, requiring expert human intervention, analysis and judgment. Here we carry out a spatio-temporal fractal analysis of a metropolitan area, based on which we develop a model which generates a cartographic representation and classification of built-up areas, identifying (and even predicting) those areas requiring the most proximate planning and regulation. Furthermore, we show how different types of urban areas identified by the model co-evolve with the city, requiring policy regulation to be flexible and adaptive, acting just in time. The algorithmic implementation of the model is applicable to any built-up area and simple enough to pave the way for the automatic classification of urban areas worldwide.

Information about green spaces available in a city is essential for urban planning. Urban green areas are generally assessed through environmental indicators that reflect the city’s quality of life and urban comfort. A methodology based on 3D measure and analysis of green urban areas at the city scale is presented. Two products are proposed: (1) measuring current vegetation cover at ground level through object-oriented classification of WorldView-2 imagery; and (2) estimating potential green cover at rooftop level using 3D data obtained by LiDAR sensor. The methodology, implemented in Lisbon, Portugal, demonstrates that: (1) remote sensing imagery provides powerful tools for master planning and policy analysis regarding green urban area expansion; and (2) measures of urban sustainability cannot be solely based on indicators obtained from 2D geographical information. In fact, 2D urban indicators should be complemented by 3D modelling of geographic data.