Details

Machine Learning and the City

Applications in Architecture and Urban Design
1. Aufl.

von: Silvio Carta
91,99 €
Verlag:	Wiley-Blackwell
Format:	PDF
Veröffentl.:	11.05.2022
ISBN/EAN:	9781119749585
Sprache:	englisch
Anzahl Seiten:	672

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Beschreibungen

Titelbeschreibung

Machine Learning and the City Explore the applications of machine learning and artificial intelligence to the built environment Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: <ul><li>An introduction to the probabilistic logic that underpins machine learning</li> <li>Comprehensive explorations of the applications of machine learning and artificial intelligence to urban environments</li> <li>Practical discussions of the consequences of applied machine learning and the future of urban design</li></ul> Perfect for designers approaching machine learning and AI for the first time, Machine Learning and the City: Applications in Architecture and Urban Design will also earn a place in the libraries of urban planners and engineers involved in urban design.

Inhaltsverzeichnis

Preface xiii Acknowledgements xv Introduction xvi Section I Urban Complexity 1 1 Urban Complexity 3 Sean Hanna 2 Emergence and Universal Computation 15 Cassey Lee 3 Fractals and Geography 31 Pierre Frankhauser and Denise Pumain Project 1 Emergence and Urban Analysis 57 Ljubomir Jankovic Project 2 The Evolution and Complexity of Urban Street Networks 63 Nahid Mohajeri and Agust Gudmundsson Section II Machines that Think 69 4 Artificial Intelligence, Logic, and Formalising Common Sense 71 John McCarthy 5 Defining Artificial Intelligence 91 David B. Fogel 6 AI: From Copy of Human Brain to Independent Learner 121 Shelly Fan 7 The History of Machine Learning and Its Convergent Trajectory Towards AI 129 Keith D. Foote 8 Machine Behaviour 143 Iyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-François Bonnefon, Cynthia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. Jackson, Nicholas R. Jennings, Ece Kamar, Isabel M. Kloumann, Hugo Larochelle, David Lazer, Richard McElreath, Alan Mislove, David C. Parkes, Alex ‘Sandy’ Pentland, Margaret E. Roberts, Azim Shariff, Joshua B. Tenenbaum, and Michael Wellman Project 3 Plan Generation from Program Graph 167 Ao Li, Runjia Tian, Xiaoshi Wang, and Yueheng Lu Project 4 Self-organising Floor Plans in Care Homes 171 Silvio Carta, Stephanie St. Loe, Tommaso Turchi, and Joel Simon Project 5 N2P2 – Neural Networks and Public Places 177 Roberto Bottazzi, Tasos Varoudis, Piyush Prajapati, and Xi Wang Project 6 Urban Fictions 183 Matias del Campo, Sandra Manninger, and Alexandra Carlson Project 7 Latent Typologies: Architecture in Latent Space 189 Stanislas Chaillou Project 8 Enabling Alternative Architectures 193 Nate Peters Project 9 Distant Readings of Architecture: A Machine View of the City 201 Andrew Witt Section III How Machines Learn 207 9 What Is Machine Learning? 209 Jason Bell 10 Machine Learning: An Applied Mathematics Introduction 217 Paul Wilmott 11 Machine Learning for Urban Computing 249 Bilgeçağ Aydoğdu and Albert Ali Salah 12 Autonomous Artificial Intelligent Agents 263 Iaroslav Omelianenko Project 10 Machine Learning for Spatial and Visual Connectivity 287 Sherif Tarabishy, Stamatios Psarras, Marcin Kosicki, and Martha Tsigkari Project 11 Navigating Indoor Spaces Using Machine Learning: Train Stations in Paris 293 Zhoutong Wang, Qianhui Liang, Fabio Duarte, Fan Zhang, Louis Charron, Lenna Johnsen, Bill Cai, and Carlo Ratti Project 12 Evolutionary Design Optimisation of Traffic Signals Applied to Quito City 297 Rolando Armas, Hernán Aguirre, Fabio Daolio, and Kiyoshi Tanaka Project 13 Constructing Agency: Self-directed Robotic Environments 303 Patrik Schumacher Section IV Application to the City 309 13 Code and the Transduction of Space 311 Martin Dodge and Rob Kitchin 14 Augmented Reality in Urban Places: Contested Content and the Duplicity of Code 341 Mark Graham, Matthew Zook, and Andrew Boulton 15 Spatial Data in Urban Informatics: Contentions of the Software-sorted City 367 Marcus Foth, Fahame Emamjome, Peta Mitchell, and Markus Rittenbruch 16 Urban Morphology Meets Deep Learning: Exploring Urban Forms in One Million Cities, Towns, and Villages Across the Planet 379 Vahid Moosavi 17 Computational Urban Design: Methods and Case Studies 393 Snoweria Zhang and Luc Wilson 18 Indexical Cities: Personal City Models with Data as Infrastructure 409 Diana Alvarez-Marin 19 Machine Learning, Artificial Intelligence, and Urban Assemblages 445 Serjoscha Düring, Reinhard Koenig, Nariddh Khean, Diellza Elshani, Theodoros Galanos, and Angelos Chronis 20 Making a Smart City Legible 453 Franziska Pilling, Haider Ali Akmal, Joseph Lindley, and Paul Coulton Project 14 A Tale of Many Cities: Universal Patterns in Human Urban Mobility 467 Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, and Cecilia Mascolo Project 15 Using Cellular Automata for Parking Recommendations in Smart Environments 473 Gwo-Jiun Horng Project 16 Gan Hadid 477 Sean Wallish Project 17 Collective Design for Collective Living 483 Elizabeth Christoforetti and Romy El Sayah Project 18 Architectural Machine Translation 489 Erik Swahn Project 19 Large-scale Evaluation of the Urban Street View with Deep Learning Method 495 Hui Wang, Elisabete A. Silva, and Lun Liu Project 20 Urban Portraits 501 Jose Luis García del Castillo y López Project 21 ML-City 507 Benjamin Ennemoser Project 22 Imaging Place Using Generative Adversarial Networks (GAN Loci) 513 Kyle Steinfeld Project 23 Urban Forestry Science 517 Iacopo Testi Section V Machine Learning and Humans 521 21 Ten Simple Rules for Responsible Big Data Research 523 Matthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, and Frank Pasquale 22 A Unified Framework of Five Principles for AI in Society 535 Luciano Floridi and Josh Cowls 23 The Big Data Divide and Its Consequences 547 Matthew T. McCarthy 24 Design Fiction: A Short Essay on Design, Science, Fact, and Fiction 561 Julian Bleecker 25 Superintelligence and Singularity 579 Ray Kurzweil 26 The Social Life of Robots: The Politics of Algorithms, Governance, and Sovereignty 603 Vincent J. Del Casino Jr, Lily House-Peters, Jeremy W. Crampton, and Hannes Gerhardt Project 24 Experiments in Synthetic Data 615 Forensic Architecture Project 25 Emotional AI in Cities: Cross-cultural Lessons from the UK and Japan on Designing for an Ethical Life 621 Vian Bakir, Nader Ghotbi, Tung Manh Ho, Alexander Laffer, Peter Mantello, Andrew McStay, Diana Miranda, Hiroshi Miyashita, Lena Podoletz, Hiromi Tanaka, and Lachlan Urquhart Project 26 Decoding Urban Inequality: The Applications of Machine Learning for Mapping Inequality in Cities of the Global South 625 Kadeem Khan Project 27 Amsterdam 2040 631 Maria Luce Lupetti Project 28 Committee of Infrastructure 635 Jason Shun Wong Index 639

Autorenportrait

Silvio Carta is an architect and Associate Professor at the University of Hertfordshire, UK. His research interests include digital architecture, data-driven approaches and computational design. Silvio is the author of Big Data, Code and the Discrete City. Shaping Public Realms (Routledge 2019).

Back cover copy

Explore the applications of machine learning and artificial intelligence to the built environment Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: <ul><li>An introduction to the probabilistic logic that underpins machine learning</li> <li>Comprehensive explorations of the applications of machine learning and artificial intelligence to urban environments</li> <li>Practical discussions of the consequences of applied machine learning and the future of urban design</li></ul> Perfect for designers approaching machine learning and AI for the first time, Machine Learning and the City: Applications in Architecture and Urban Design will also earn a place in the libraries of urban planners and engineers involved in urban design.