Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany.
German Remote Sensing Data Center, German Aerospace Center, Münchener Straße 20, Weßling, 82234, Germany.
Sci Data. 2022 Nov 19;9(1):715. doi: 10.1038/s41597-022-01780-x.
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, statistical and machine learning methods have been developed to estimate 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 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 a digital elevation model, local climate zone, land use proportions, 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 approaches in the field of population estimation.
获得动态的人口分布对于许多决策过程至关重要，例如城市规划、灾害管理，最重要的是帮助政府更好地分配社会技术供应。为了实现这些目标，良好的人口数据是必不可少的。传统的通过人口普查收集人口数据的方法既昂贵又繁琐。近年来，已经开发出统计和机器学习方法来估计人口分布。大多数方法使用的数据集要么规模较小，要么尚未公开。因此，新方法的开发和评估具有挑战性。我们通过提供一个用于 98 个欧洲城市人口估计的综合数据集来填补这一空白。该数据集包括数字高程模型、局部气候区、土地利用比例、与多光谱 Sentinel-2 图像结合的夜间灯光以及来自 Open Street Map 计划的数据。我们预计，对于开发人口估计领域的复杂方法，它将是研究界的宝贵补充。
