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Introduction -- Cities and the green economy -- Renewable energy -- Building the energy efficient city -- Trash or treasure -- Transportation -- Only connect

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Focusing on material and social forms of infrastructure, this edited collection draws on rich empirical details from cities across the global North and South. The book asks the reader to think through the different ways in which infrastructure comes to be present in cities and its co-constitutive relationships with urban inhabitants and wider processes of urbanization.

Considering the climate emergency, economic transformation, public health crises and racialized inequality, the book argues that paying attention to infrastructures' past, present and future allows us to understand and respond to the current urban condition.

Mobile and social technologies are providing new opportunities to document, characterize, and gather impressions of urban environments. In this article, we present a study that examines urban perceptions of three cities in central Mexico; the study integrates a mobile crowdsourcing framework to collect geo-localized images of urban environments by a local youth community, an online crowdsourcing platform to gather impressions of urban environments along 12 physical and psychological dimensions, and a deep learning framework to automatically infer human impressions of outdoor urban scenes. Our study resulted in a collection of 7,000 geo-localized images containing outdoor scenes and views of each city's built environment, including touristic, historical, and residential neighborhoods, and 144,000 individual judgments from Amazon Mechanical Turk. Statistical analyses show that outdoor environments can be assessed in terms of interrater agreement for most of the urban dimensions by the observers of crowdsourced images. Furthermore, we proposed a methodology to automatically infer human perceptions of outdoor scenes using a variety of low-level image features and generic deep learning (CNN) features. We found that CNN features consistently outperformed all the individual low-level image features for all the studied urban dimensions. We obtained a maximum
R
2
of 0.49 using CNN features; for 9 out of 12 labels, the obtained
R
2
values exceeded 0.44.

Cover -- Contents -- Tables, Figure, and Boxes -- Foreword -- Acknowledgments -- Abbreviations -- About the Authors -- Toward Livable Cities through Inclusive Urban Redevelopment: An Introduction -- I. ADB's Experiences -- India: ADB's Involvement in Slum Rehabilitation -- Indonesia: Neighborhood Upgrading and Shelter Sector Project-Toward Cities without Slums -- Philippines: Strategic Private Sector Partnerships for Urban Poverty Reduction in Metro Manila -- II. Inclusive Urban Redevelopment: Toward Livable Cities -- Tables -- 1 Infrastructure Financed under the Neighborhood Upgrading and Shelter Sector Project (September 2005 to December 2009) -- 2 Source of Funds for Infrastructure Upgrading under the Neighborhood Upgrading and Shelter Sector Project -- 3 Planned vs. Actual Allocations for Project Inputs -- Figure -- 1 The Housing Policy Map -- Boxes -- 1 Activities Supported by Corporate Partners -- 2 The Cities Development Initiative for Asia.