Urban heat islands, defined as specific zones exhibiting substantially higher
temperatures than their immediate environs, pose significant threats to
environmental sustainability and public health. This study introduces a novel
machine-learning model that amalgamates data from the Sentinel-3 satellite,
meteorological predictions, and additional remote sensing inputs. The primary
aim is to generate detailed spatiotemporal maps that forecast the peak
temperatures within a 24-hour period in Turin. Experimental results validate
the model's proficiency in predicting temperature patterns, achieving a Mean
Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution
of 20 meters per pixel, thereby enriching our knowledge of urban climatic
behavior. This investigation enhances the understanding of urban microclimates,
emphasizing the importance of cross-disciplinary data integration, and laying
the groundwork for informed policy-making aimed at alleviating the negative
impacts of extreme urban temperatures.

通过使用来自 Sentinel-3 卫星、气象预测和其他遥感输入的数据，本研究介绍了一种新颖的机器学习模型，旨在生成详细的时空地图，预测都灵市 24 小时内的最高温度。实验结果表明，该模型在预测温度模式方面表现出色，对于 2023 年分辨率为每像素 20 米的情况，均方根误差为 2.09 摄氏度，从而丰富了我们对城市气候行为的认识。这项研究增进了我们对城市微气候的理解，强调了跨学科数据整合的重要性，并为缓解极端城市温度的负面影响的知情决策制定奠定了基础。

利用卷积神经网络通过遥感数据进行最高温度预测

Maximum Temperature Prediction Using Remote Sensing Data Via  Convolutional Neural Network

Global urbanization has underscored the significance of urban microclimates
for human comfort, health, and building/urban energy efficiency. They
profoundly influence building design and urban planning as major environmental
impacts. Understanding local microclimates is essential for cities to prepare
for climate change and effectively implement resilience measures. However,
analyzing urban microclimates requires considering a complex array of outdoor
parameters within computational domains at the city scale over a longer period
than indoors. As a result, numerical methods like Computational Fluid Dynamics
(CFD) become computationally expensive when evaluating the impact of urban
microclimates. The rise of deep learning techniques has opened new
opportunities for accelerating the modeling of complex non-linear interactions
and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown
to be very promising in accelerating solving the Partial Differential Equations
(PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO
network for real-time three-dimensional (3D) urban wind field simulation. The
training and testing data are generated from CFD simulation of the urban area,
based on the semi-Lagrangian approach and fractional stepping method to
simulate urban microclimate features for modeling large-scale urban problems.
Numerical experiments show that the FNO model can accurately reconstruct the
instantaneous spatial velocity field. We further evaluate the trained FNO model
on unseen data with different wind directions, and the results show that the
FNO model can generalize well on different wind directions. More importantly,
the FNO approach can make predictions within milliseconds on the graphics
processing unit, making real-time simulation of 3D dynamic urban microclimate
possible.

利用 Fourier 神经算子 (FNO) 网络以实时三维城市风场模拟，通过半拉格朗日方法和分数步进法模拟大规模城市问题中的城市微气候特征，数值实验显示 FNO 模型能够准确重构瞬时空间速度场，在不同风向的未见数据上也具有良好的泛化能力，更重要的是 FNO 方法能够在毫秒内进行预测，实现了三维动态城市微气候的实时模拟。