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

Nosratabadi, Saeed; Mosavi, Amir; Keivani, Ramin; Ardabili, Sina Faizollahzadeh; Aram, Farshid

doi:10.31219/osf.io/gmuzk

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…Generative Adversarial Networks (GAN) have been utilized for simulating urban patterns [38,39]. Contributions from deep learning (DL) and machine learning (ML) methods to the evolution of models in various aspects of prediction, planning, and uncertainty analysis in smart cities and urban development have been notable [40,41], providing support for urban planning and decision making [42].…”

Section: Application Of Machine Learning In Urban Perceptionmentioning

confidence: 99%

Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

Zhang

et al. 2023

Land

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Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city’s overall functionality. This research scrutinizes the perceptual biases and disparities in architectural function distribution within urban spaces, with a particular focus on Shanghai’s central urban district. The study employs machine learning to clarify these biases within urban spatial perception research, utilizing a tripartite methodology: objective mapping, subjective perception analysis, and perception deviation assessment. The study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Furthermore, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. Our findings underscore the need for urban planning strategies that align with human perceptual needs, thereby enhancing the quality of the urban environment and fostering a more habitable and sustainable urban space. The study’s implications suggest that a deeper understanding of perceptual needs can optimize architectural function distribution, enhancing the urban environment’s quality.

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Section: Application Of Machine Learning In Urban Perceptionmentioning

confidence: 99%

Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

Zhang

et al. 2023

Land

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“…At the same time, advances in computer science theory, mathematical research, and processing power equip us with the tools we need to gather the data (Milojevic-Dupont and Creutzig, 2021). New technologies and their applications have shown to be extremely advantageous in terms of fostering a better future in dealing with the challenges of future cities (Nosratabadi et al, 2019). Artificial intelligence applications such as machine learning (ML) have become increasingly popular in recent years as a versatile tool for technological advancement (Rolnick et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques have contributed to a variety of application fields, such as transportation management, urban planning, distribution of resources, forecasting energy requirements, forecasting food security and supply, and monitoring as well as forecasting poor air quality, etc. (Audu et al, 2020; Nosratabadi et al, 2019; Wataya and Shaw, 2019). Given that the climate change issue is extremely complex and necessitates multidisciplinary research (Huntingford et al, 2019), the usage of ML techniques has shown to be a helpful tool for realizing and predicting climate-related issues (Koc and Acar 2021).…”

Section: Introductionmentioning

confidence: 99%

Applications of machine learning and deep learning methods for climate change mitigation and adaptation

Ladi

Jabalameli

Sharifi

2022

Environment and Planning B: Urban Analytics and City Science

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Climate change is a global issue that must be considered and addressed immediately. Many articles have been published on climate change mitigation and adaptation. However, new methods are required to explore the complexities of climate change and provide more efficient and effective adaptation and mitigation policies. With the advancement of technology, machine learning (ML) and deep learning (DL) methods have gained considerable popularity in many fields, including climate change. This paper aims to explore the most popular ML and DL methods that have been applied for climate change mitigation and adaptation. Another aim is to determine the most common mitigation and adaptation measures/actions in general, and in urban areas in particular, that have been studied using ML and DL methods. For this purpose, word frequency analysis and topic modeling, specifically the Latent Dirichlet allocation (LDA) as a ML algorithm, are used in this study. The results indicate that the most popular ML technique in both climate change mitigation and adaptation is the Artificial Neural Network. Moreover, among different research areas related to climate change mitigation and adaptation, geoengineering, and land surface temperature are the ones that have used ML and DL algorithms the most.

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“…Other existing surveys have explored the different applications of Deep RL, e.g. in healthcare (Yu et al, 2019), communications/networking (Luong et al, 2019), video games (Justesen et al, 2019;Skinner & Walmsley, 2019), cyber security (Nguyen & Reddi, 2019), natural language processing (Whiteson, 2019;Stylianou & Vlahavas, 2019), robotics (Tai et al, 2016), biology (Mahmud et al, 2018), urban sustainability/transportation (Nosratabadi et al, 2020), agriculture (Yang & Sun, 2019), and many others (Li, 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning: A State-of-the-Art Walkthrough

Lazaridis¹,

Fachantidis²,

Vlahavas

2020

jair

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Deep Reinforcement Learning is a topic that has gained a lot of attention recently, due to the unprecedented achievements and remarkable performance of such algorithms in various benchmark tests and environmental setups. The power of such methods comes from the combination of an already established and strong field of Deep Learning, with the unique nature of Reinforcement Learning methods. It is, however, deemed necessary to provide a compact, accurate and comparable view of these methods and their results for the means of gaining valuable technical and practical insights. In this work we gather the essential methods related to Deep Reinforcement Learning, extracting common property structures for three complementary core categories: a) Model-Free, b) Model-Based and c) Modular algorithms. For each category, we present, analyze and compare state-of-the-art Deep Reinforcement Learning algorithms that achieve high performance in various environments and tackle challenging problems in complex and demanding tasks. In order to give a compact and practical overview of their differences, we present comprehensive comparison figures and tables, produced by reported performances of the algorithms under two popular simulation platforms: the Atari Learning Environment and the MuJoCo physics simulation platform. We discuss the key differences of the various kinds of algorithms, indicate their potential and limitations, as well as provide insights to researchers regarding future directions of the field.

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

Cited by 10 publications

References 33 publications

Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

Applications of machine learning and deep learning methods for climate change mitigation and adaptation

Deep Reinforcement Learning: A State-of-the-Art Walkthrough

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