So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 46 references that are in the map. First posted on arXiv 7 Apr 2022.
Builds on
3 of 46 references.
References · 3
- 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 110
- 2017A Deep Learning Approach for Population Estimation from Satellite Imagerycited by 15
- 2019Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagerycited by 9
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
2 papers.
- 2022EarthNets: Empowering AI in Earth Observationcited by 40
- 2026A Hybrid State-Space Approach for Census-Tract Population Estimationcited by 0
- 2026A Hybrid State-Space Approach for Census-Tract Population Estimation30 Aug 2026
- 2022EarthNets: Empowering AI in Earth Observation10 Oct 2022