8 used a random forest approach to estimate the population at ~100 m resolution for Vietnam, Cambodia, and Kenya.

In general, these methods have been applied either to a smaller region or based on some country-specific data such as building footprints or other detailed geospatial data sets which are not easily available in other countries. In recent years, statistical and machine learning methods have been applied directly to remote sensing data to estimate the population distribution 6, 7. The accuracy of the data depends upon the number and size of the administrative unit, the collection method, the completeness of the survey, and varies significantly within the rural and urban regions 6. In this procedure, the population data is systematically collected and compiled over an administrative unit or a census unit. Traditionally, population estimation is done through a census. To understand the key trends in urbanization, population estimation plays a crucial role. Rapid urbanization in cities is leading to environmental concerns such as climatic changes, food and water scarcity, poor air quality, deforestation, etc 1, 2, 3, 4, 5. 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. 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 fill this gap by providing a comprehensive data set for population estimation in 98 European cities. Thus, the development and evaluation of new methods become challenging. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. In recent years, statistical and machine learning methods have been developed to estimate population distribution. The traditional method of collecting population data through the census is expensive and tedious. For the aspiration of these objectives, good population data is essential. 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.

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