With the global population on the rise, our cities have been expanding to
accommodate the growing number of people. The expansion of cities generally
leads to the engulfment of peripheral areas. However, such expansion of urban
areas is likely to cause increment in areas with increased land surface
temperature (LST). By considering each summer as a data point, we form LST
multi-year time-series and cluster it to obtain spatio-temporal pattern. We
observe several interesting phenomena from these patterns, e.g., some clusters
show reasonable similarity to the built-up area, whereas the locations with
high temporal variation are seen more in the peripheral areas. Furthermore, the
LST center of mass shifts over the years for cities with development activities
tilted towards a direction. We conduct the above-mentioned studies for three
different cities in three different continents.

城市扩张导致了周边地区的淹没，而这种城市区域的扩张很可能导致土地表面温度增加。通过考虑每年夏季作为一个数据点，我们形成土地表面温度多年时间序列并进行聚类以获取时空模式。从这些模式中，我们观察到一些有趣的现象，如一些聚类与建筑区域具有合理的相似性，而具有高时空变化的位置更多地出现在周边地区。此外，城市发展活动倾向于某个方向，而土地表面温度的质心随着时间的推移也会发生变化。我们对三个不同大洲的三个城市进行了上述研究。

城市地区的演变与地表温度

Evolution of urban areas and land surface temperature

Time-series remote sensing data offer a rich source of information that can
be used in a wide range of applications, from monitoring changes in land cover
to surveilling crops, coastal changes, flood risk assessment, and urban sprawl.
This paper addresses the challenge of using time-series satellite images to
predict urban expansion. Building upon previous work, we propose a novel
two-step approach based on semantic image segmentation in order to predict
urban expansion. The first step aims to extract information about urban regions
at different time scales and prepare them for use in the training step. The
second step combines Convolutional Neural Networks (CNN) with Long Short Term
Memory (LSTM) methods in order to learn temporal features and thus predict
urban expansion. In this paper, experimental results are conducted using
several multi-date satellite images representing the three largest cities in
Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. We empirically evaluated our
proposed technique, and examined its results by comparing them with
state-of-the-art approaches. Following this evaluation, we determined that our
results reveal improved performance for the new-coupled CNN-LSTM approach,
particularly in terms of assessments based on Mean Square Error, Root Mean
Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and
overall classification accuracy.

本文提出了一种基于语义图像分割的新型两步方法来预测城市扩张，结合卷积神经网络和长短时记忆方法以学习时间特征，以多日期卫星影像数据在沙特阿拉伯的三个城市进行了实验评估，结果显示新的联合卷积神经网络 - 长短时记忆方法具有更好的性能表现。