Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments

Building and Environment, Год журнала: 2024, Номер unknown, С. 112088 - 112088

Опубликована: Сен. 1, 2024

Язык: Английский

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SolarGAN for Meso-Level Solar Radiation Prediction at the Urban Scale: A Case Study in Boston

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4524 - 4524

Опубликована: Дек. 2, 2024

Evaluating solar radiation distribution at the urban scale is crucial for optimizing placement and size of installations managing heat. This study introduces a method predicting using 2D mapping data, applying Generative Adversarial Network (GAN) model to city Boston. Traditional simulation methods, such as 3D modeling satellite imagery, require complex resource-intensive data inputs. In contrast, this research allows open-source geographic information—such building footprints, heights, terrain—to predict various spatial scales (150 m, 300 500 m). The GAN model, detailed results, trained paired datasets information heatmaps. It achieved high accuracy resolution, with m demonstrating best performance (R2 = 0.864). model’s capability generate high-resolution (2 m) maps from simplified inputs demonstrates potential GANs climate prediction, offering rapid efficient alternative traditional methods. approach holds significant planning, particularly in photovoltaic (PV) system layouts UHI effect.

Язык: Английский

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SolarGAN For Meso-Level Solar Radiation Prediction at the Urban-Scale: A Case Study in Boston

Опубликована: Янв. 1, 2024

Язык: Английский

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How Do 3d Urban Morphological Characteristics Spatiotemporally Affect the Urban Thermal Environment? A Case Study of San Antonio

Опубликована: Янв. 1, 2024

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Язык: Английский

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Analyzing Pocket Park Locations and Pedestrian Accident Rates Using Generative Adversarial Networks

Опубликована: Янв. 1, 2024

Язык: Английский

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