Building layout generation using site-embedded GAN model

Automation in Construction, Год журнала: 2023, Номер 151, С. 104888 - 104888

Опубликована: Апрель 25, 2023

Язык: Английский

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Automated site planning using CAIN-GAN model

Automation in Construction, Год журнала: 2024, Номер 159, С. 105286 - 105286

Опубликована: Янв. 13, 2024

Язык: Английский

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Global urban road network patterns: Unveiling multiscale planning paradigms of 144 cities with a novel deep learning approach

Landscape and Urban Planning, Год журнала: 2023, Номер 241, С. 104901 - 104901

Опубликована: Сен. 30, 2023

Язык: Английский

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A study on urban block design strategies for improving pedestrian-level wind conditions: CFD-based optimization and generative adversarial networks

Energy and Buildings, Год журнала: 2023, Номер 304, С. 113863 - 113863

Опубликована: Дек. 24, 2023

Язык: Английский

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Multi-level urban street representation with street-view imagery and hybrid semantic graph

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 218, С. 19 - 32

Опубликована: Окт. 18, 2024

Язык: Английский

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Advancing Synergistic Urban Heat Island Mitigation Based on Multimodal Data Integration and a Novel Cyclegan-Pix2pix(Cp-Gan) Model

Опубликована: Янв. 1, 2025

Язык: Английский

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Interpreting core forms of urban morphology linked to urban functions with explainable graph neural network

Computers Environment and Urban Systems, Год журнала: 2025, Номер 118, С. 102267 - 102267

Опубликована: Фев. 24, 2025

Язык: Английский

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Formalising the urban pattern language: A morphological paradigm towards understanding the multi-scalar spatial structure of cities

Cities, Год журнала: 2025, Номер 161, С. 105854 - 105854

Опубликована: Март 5, 2025

Язык: Английский

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Generative Adversarial Networks for Climate-Sensitive Urban Morphology: An Integration of Pix2Pix and the Cycle Generative Adversarial Network

Land, Год журнала: 2025, Номер 14(3), С. 578 - 578

Опубликована: Март 10, 2025

Urban heat island (UHI) effects pose significant challenges to sustainable urban development, necessitating innovative modeling techniques optimize morphology for thermal resilience. This study integrates the Pix2Pix and CycleGAN architectures generate high-fidelity models aligned with local climate zones (LCZs), enhancing their applicability studies. research focuses on eight major Chinese coastal cities, leveraging a robust dataset of 4712 samples train generative models. Quantitative evaluations demonstrated that integration substantially improved structural fidelity realism in synthesis, achieving peak Structural Similarity Index Measure (SSIM) 0.918 coefficient determination (R2) 0.987. The total adversarial loss training stabilized at 0.19 after 811 iterations, ensuring high convergence structure generation. Additionally, CycleGAN-enhanced outputs exhibited 35% reduction relative error compared Pix2Pix-generated images, significantly improving edge preservation feature accuracy. By incorporating LCZ data, proposed framework successfully bridges climate-responsive planning, enabling adaptive design strategies mitigating UHI effects. enhance generation, while classification produce forms align specific climatological conditions. Compared model trained by coupled alone, approach offers planners more precise tool designing optimizing layouts mitigate effects, improve energy efficiency,

Язык: Английский

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How geospatial technologies are transforming urban net-zero energy buildings: a comprehensive review of insights, challenges, and future directions

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112357 - 112357

Опубликована: Март 1, 2025

Язык: Английский

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