Artificial Intelligence in Landscape Architecture: A Survey
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Abstract (arXiv)
The development history of landscape architecture (LA) reflects the human pursuit of environmental beautification and ecological balance. With the advancement of artificial intelligence (AI) technologies that simulate and extend human intelligence, immense opportunities have been provided for LA, offering scientific and technological support throughout the entire workflow. In this article, we comprehensively review the applications of AI technology in the field of LA. First, we introduce the many potential benefits that AI brings to the design, planning, and management aspects of LA. Secondly, we discuss how AI can assist the LA field in solving its current development problems, including urbanization, environmental degradation and ecological decline, irrational planning, insufficient management and maintenance, and lack of public participation. Furthermore, we summarize the key technologies and practical cases of applying AI in the LA domain, from design assistance to intelligent management, all of which provide innovative solutions for the planning, design, and maintenance of LA. Finally, we look ahead to the problems and opportunities in LA, emphasizing the need to combine human expertise and judgment for rational decision-making. This article provides both theoretical and practical guidance for LA designers, researchers, and technology developers. The successful integration of AI technology into LA holds great promise for enhancing the field's capabilities and achieving more sustainable, efficient, and user-friendly outcomes.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 212 references that are in the map. First posted on arXiv 26 Aug 2024.
Builds on
22 of 212 references.
References · 22
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
- 2020Image Segmentation Using Deep Learning: A Surveycited by 659
- 2017Generative Adversarial Networks: An Overviewcited by 588
- 2015Recent Advances in Convolutional Neural Networkscited by 360
- 2018Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Modelscited by 118
- 2018A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspectivecited by 111
- 2016Deep Learning the City : Quantifying Urban Perception At A Global Scalecited by 65
- 2023AI-Generated Content (AIGC): A Surveycited by 59
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- 2020Sentiment Analysis Based on Deep Learning: A Comparative Studycited by 37
- 2019Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countriescited by 20
- 2023Data Scarcity in Recommendation Systems: A Surveycited by 17
- 2023Web 3.0: The Future of Internetcited by 11
- 2018HUOPM: High Utility Occupancy Pattern Miningcited by 10
- 2022Metaverse: Survey, Applications, Security, and Opportunitiescited by 7
- 2019Utility Mining Across Multi-Dimensional Sequencescited by 5
- 2021Anomaly Rule Detection in Sequence Datacited by 5
- 2024Geospatial Big Data: Survey and Challengescited by 5
- 2023Open Metaverse: Issues, Evolution, and Futurecited by 2
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