State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

TL;DR

This survey reviews recent deep learning and machine learning techniques applied to smart cities, highlighting key methods and domains such as energy, health, and urban transport, and introduces a new taxonomy for categorization.

Contribution

It presents a novel taxonomy of DL and ML methods in smart cities and summarizes recent advances and application domains in urban sustainability.

Findings

Five main DL and ML methods are most used in smart cities.

Energy, health, and urban transport are primary application domains.

Deep learning and ML significantly contribute to urban sustainability challenges.

Abstract

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.