Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges

Sesil Koutra and Christos S. Ioakimidis ()
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Sesil Koutra: Faculty of Architecture and Urban Planning, University of Mons, 88 Str. Havré, 7000 Mons, Belgium
Christos S. Ioakimidis: Inteligg P.C., Karaiskaki 28, 10554 Athens, Greece

Land, 2022, vol. 12, issue 1, 1-19

Abstract: In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies. In this work, a comprehensive and in-depth review is presented to discuss the future opportunities and constraints in meeting the next planning portfolio against the multiple challenges in urban environments in line with Machine Learning progress. Bringing together the theoretical views with practical analyses of cases and examples, the work unveils the huge potential, but also the potential barriers of the complexity of Machine Learning to urban planning strategies.

Keywords: case-study analysis; machine learning; urban planning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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