Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems : potential, issues and challenges
Publié dansJournal of King Saud University - Computer and Information Sciences
Le document apparaît dansProduction scientifique et compétences > ENAC - Faculté de l'environnement naturel, architectural et construit > IA - Institut d'architecture et de la ville > CEAT - Communauté d'études pour l'aménagement du territoire
Publications validées par des pairs
Travail produit à l'EPFL
Articles de journaux
Date de création de la notice2021-08-23
Modern cities dynamically face several challenges including digitalization, sustainability, resilience and economic development. Urban planners and designers must develop urban forms that address these challenges. With the integration of new communication and information technologies (Smartphone, GIS, Drones, IoT, Sensors, etc.), urban activities have generated large volumes of urban data. The rapid growth in terms of collection and big data storage capacities combined with the ever-increasing computational power of modern machines have made possible their efficient treatment using machine (ML) and deep learning (DL) algorithms. The emergence of such groundbreaking methods has in turn helped to address the challenges of modern-day cities in several domains (health, security, mobility, etc). ML algorithms have been proposed to model the urban form’s indicators for intelligent urban planning decision making. They have been proven to perform better than the traditional methods. However, the potential of ML has not yet been fully explored in research for urban planning decision support. This paper presents a comprehensive review of ML applications for mitigating the challenges of modern cities planning. First and foremost, an overview of the urban forms, sources of urban data, the ML and DL techniques as well as their potential in solving the aforementioned challenges. For each ML method, we will highlight it working principle, advantages, disadvantages and potential applications using comparative tables. Finally, we will discuss the issues and challenges of ML methods in urban form’s modeling while ultimately advocating some future research directions.