Chapter 11 - Image analytics for urban planning: The case of the Barcelona Superblock

Abstract

Rising levels of air contamination are dictating urgent actions by administrators and urban planners. To face this unprecedented threat, cities need to reshape their urban patterns, reprogram mobility, public spaces, and urban infrastructures toward safer, healthier, and ecological solutions. The city of Barcelona is today ranked among the most polluted cities in Europe. To reverse this trend, in 2016, Barcelona introduced a new urban planning model called the Superblock. This planning approach threatens the urban fabric as a programmable surface, regulating the access to cars and vehicles in public streets while enabling the extension of walkable areas and cycling paths. Concurrently to the development of new urban strategies, novel data-driven instruments have been emerging, providing different approaches to inform spatial planning. This chapter highlights several techniques, triggered by computer vision and machine learning algorithms, to analyze image-based data, extracting meaningful metrics to inform spatial transformations and estimate CO2 emissions in an urban environment. This methodology can enrich emergent approaches of urban planning, such as the Superblock, with a novel set of metrics and criteria based on real-time spatial usage and carbon footprint to guide and orient future urban transformations, ultimately improving the implementation of more resilient, ecological, and sustainable urban models.