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

Nowadays, thermal infrared satellite remote sensors enable to extract very
interesting information at large scale, in particular Land Surface Temperature
(LST). However such data are limited in spatial and/or temporal resolutions
which prevents from an analysis at fine scales. For example, MODIS satellite
provides daily acquisitions with 1Km spatial resolutions which is not
sufficient to deal with highly heterogeneous environments as agricultural
parcels. Therefore, image super-resolution is a crucial task to better exploit
MODIS LSTs. This issue is tackled in this paper. We introduce a deep
learning-based algorithm, named Multi-residual U-Net, for super-resolution of
MODIS LST single-images. Our proposed network is a modified version of U-Net
architecture, which aims at super-resolving the input LST image from 1Km to
250m per pixel. The results show that our Multi-residual U-Net outperforms
other state-of-the-art methods.

本论文研究了如何使用深度学习算法，特别是多重残差 U-Net 模型，对 MODIS LST 遥感图像进行超分辨率处理，从而实现将遥感图像从 1Km 提高到 250m 的像素分辨率。实验结果表明，多重残差 U-Net 模型表现优于其他目前主流的方法。