Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, machine learning methods have been developed to estimate the population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of the new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises digital elevation model, local climate zone, land use classifications, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated machine learning-based approaches in the field of population estimation.

研究人口分布，提供包括数字高程模型、本地气候区域、土地利用比例、夜间灯光和Sentinel-2的多光谱图像以及Open Street Map计划的数据，为人口估计的研究提供了一个全面的数据集。

So2Sat POP -- 大陆尺度空间人口估算的数据集