Urban Air Temperature Prediction using Conditional Diffusion Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916529577132032 |
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| author | Dai, Siyang Liu, Jun Cheung, Ngai-Man |
| author_facet | Dai, Siyang Liu, Jun Cheung, Ngai-Man |
| contents | Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature ($T_a$) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects $T_a$ is a critical research question which requires high-resolution (HR) $T_a$ data at neighborhood scale. However, weather stations providing $T_a$ measurements are sparsely distributed e.g. more than 10km apart; and numerical models are impractically slow and computationally expensive. In this work, we propose a novel method to predict HR $T_a$ at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from satellite imagery. Our method leverages diffusion models for the first time to generate accurate and visually realistic HR $T_a$ maps, which outperforms prior methods. We pave the way for meteorological research using computer vision techniques by providing a dataset of an extended spatial and temporal coverage, and a high spatial resolution as a benchmark for future research. Furthermore, we show that our model can be applied to urban planning by simulating the impact of different urban designs on $T_a$. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_13504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Urban Air Temperature Prediction using Conditional Diffusion Models Dai, Siyang Liu, Jun Cheung, Ngai-Man Computer Vision and Pattern Recognition Image and Video Processing Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature ($T_a$) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects $T_a$ is a critical research question which requires high-resolution (HR) $T_a$ data at neighborhood scale. However, weather stations providing $T_a$ measurements are sparsely distributed e.g. more than 10km apart; and numerical models are impractically slow and computationally expensive. In this work, we propose a novel method to predict HR $T_a$ at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from satellite imagery. Our method leverages diffusion models for the first time to generate accurate and visually realistic HR $T_a$ maps, which outperforms prior methods. We pave the way for meteorological research using computer vision techniques by providing a dataset of an extended spatial and temporal coverage, and a high spatial resolution as a benchmark for future research. Furthermore, we show that our model can be applied to urban planning by simulating the impact of different urban designs on $T_a$. |
| title | Urban Air Temperature Prediction using Conditional Diffusion Models |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2412.13504 |