Artificial intelligence and sustainable development goals: Systematic literature review of the construction industry

Regona, Massimo; Yigitcanlar, Tan; Hon, Carol; Teo, Melissa

doi:10.1016/j.scs.2024.105499

Sustainable Cities and Society

2024

DOI: 10.1016/j.scs.2024.105499

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Artificial intelligence and sustainable development goals: Systematic literature review of the construction industry

Massimo Regona,

Tan Yigitcanlar,

Carol Hon

et al.

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Cited by 20 publications

References 83 publications

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Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures

Marasinghe,

Yigitcanlar,

Mayere

et al. 2024

J geovis spat anal

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Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, urban AI implementation faces unique challenges. Academic literature on responsible AI largely focuses on general principles, with limited emphasis on the geospatial domain. This important gap in scholarly work could hinder effective AI integration in urban geospatial applications. Our study employs a multi-method approach, including a systematic academic literature review, word frequency analysis and insights from grey literature, to examine potential challenges and propose strategies for effective geospatial AI (GeoAI) integration. We identify a range of responsible practices relevant to the complexities of using AI in urban geospatial planning and its effective implementation. The review provides a comprehensive and actionable framework for responsible AI adoption in the geospatial domain, offering a roadmap for urban researchers and practitioners. It highlights ways to optimise AI benefits while minimising potential negative consequences, contributing to urban sustainability and equity.

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Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures

Marasinghe,

Yigitcanlar,

Mayere

et al. 2024

J geovis spat anal

View full text Add to dashboard Cite

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A systematic review of current AI techniques used in the context of the SDGs

Greif,

Röckel,

Kimmig

et al. 2024

Int J Environ Res

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This study aims to explore the application of artificial intelligence (AI) in the resolution of sustainability challenges, with a specific focus on environmental studies. Given the rapidly evolving nature of this field, there is an urgent need for more frequent and dynamic reviews to keep pace with the innovative applications of AI. Through a systematic analysis of 191 research articles, we classified AI techniques applied in the field of sustainability. Our review found that 65% of the studies applied supervised learning methods, 18% employed unsupervised learning, and 17% utilized reinforcement learning approaches. The review highlights that artificial neural networks (ANN), are the most commonly applied AI techniques in sustainability contexts, accounting for 23% of the reviewed methods. This comprehensive overview of AI techniques identifies key trends and proposes new research avenues to address the complex issue of achieving the Sustainable Development Goals (SDGs). Graphic abstract

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Optimizing construction and demolition waste management in Nigeria: challenges, regulatory frameworks, and policy solutions

Unegbu,

Yawas

2024

Discov Civ Eng

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Artificial intelligence and sustainable development goals: Systematic literature review of the construction industry

Cited by 20 publications

References 83 publications

Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures

Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures

A systematic review of current AI techniques used in the context of the SDGs

Optimizing construction and demolition waste management in Nigeria: challenges, regulatory frameworks, and policy solutions

Contact Info

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