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30. Assessing variations in land cover-land use and surface temperature dynamics for Dehradun, India, using multi-time and multi-sensor landsat data K. Mishra & R. Garg 10.1007/s10661-023-10945-z
31. Optimisation Model for Spatialisation of Population Based on Human Footprint Index Correction D. Ren et al. 10.3390/ijgi13120429
32. Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration Z. Wang et al. 10.3390/su12010174
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34. Unraveling Long-Term Flood Risk Dynamics Across the Murray-Darling Basin Using a Large-Scale Hydraulic Model and Satellite Data S. Ceola et al. 10.3389/frwa.2021.797259
35. Multi-Scale Dynamics and Spatial Consistency of Economy and Population Based on NPP/VIIRS Nighttime Light Data and Population Imagery: A Case Study of the Yangtze River Delta Y. Xu et al. 10.3390/rs16152806
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37. Population, land use and economic exposure estimates for Europe at 100 m resolution from 1870 to 2020 D. Paprotny & M. Mengel 10.1038/s41597-023-02282-0
38. Global population datasets overestimate flood exposure in Sweden K. Karagiorgos et al. 10.1038/s41598-024-71330-5
39. A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments S. Georganos et al. 10.1016/j.jag.2022.103013
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41. Evaluating the impact of gridded population datasets variability on flood exposure estimates across South Asia J. Zhang et al. 10.1080/19475705.2025.2481996
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43. Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States J. Uhl et al. 10.5194/essd-13-119-2021
44. Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany S. Eichhorn 10.3390/su12103976
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49. Assessing the relative accuracy of gridded population sampling: results from an election survey experiment S. Ford & M. Kirwin 10.1080/13645579.2022.2091200
50. The role of connectivity on malaria dynamics across areas with contrasting control coverage in the Peruvian Amazon G. Carrasco-Escobar et al. 10.1371/journal.pntd.0012560
51. Mapping National‐Scale Road Surface Types Using Multisource Open Data and Deep Learning Model Q. Zhou et al. 10.1111/tgis.13305
52. GHS-POP Accuracy Assessment: Poland and Portugal Case Study B. Calka & E. Bielecka 10.3390/rs12071105
53. Combining expert and crowd-sourced training data to map urban form and functions for the continental US M. Demuzere et al. 10.1038/s41597-020-00605-z
54. High-resolution population estimation using household survey data and building footprints G. Boo et al. 10.1038/s41467-022-29094-x
55. The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets M. Kuffer et al. 10.3390/ijgi11070403
56. Exploring spatial feedbacks between adaptation policies and internal migration patterns due to sea-level rise L. Reimann et al. 10.1038/s41467-023-38278-y
57. Global gridded population datasets systematically underrepresent rural population J. Láng-Ritter et al. 10.1038/s41467-025-56906-7
58. Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale M. Aquilino et al. 10.3390/rs13142835
59. Different Spatiotemporal Patterns in Global Human Population and Built‐Up Land J. Gao & B. O'Neill 10.1029/2020EF001920
60. Estimation of pixel-level seismic vulnerability of the building environment based on mid-resolution optical remote sensing images X. Fan et al. 10.1016/j.jag.2021.102339
61. Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics R. Chen et al. 10.3390/ijgi9110637
62. A novel urban population-land coupling simulation model: Integrating spatial equilibrium and macro-micro strategies Y. Guo et al. 10.1016/j.jag.2024.104002
63. Warming-induced cryosphere changes predict drier Andean eco-regions A. Al-Yaari et al. 10.1088/1748-9326/ad6ea6
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65. Global population profile of tropical cyclone exposure from 2002 to 2019 R. Jing et al. 10.1038/s41586-023-06963-z
66. Exploring the effects of different population projection datasets on global compound drought and heatwave exposure estimates under shared socioeconomic pathways J. Zhang et al. 10.1088/1748-9326/adc74d
67. Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method* W. Guo et al. 10.1080/17538947.2023.2233493
68. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic C. Smith & J. Mennis 10.5888/pcd17.200246
69. Evaluating spatial statistical and machine learning models in urban dynamic population mapping Z. Cheng et al. 10.1177/27541231221114169
70. LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022 V. Lebakula et al. 10.1038/s41597-025-04817-z
71. Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface R. Swanwick et al. 10.1038/s41597-022-01603-z
72. Population development as a driver of coastal risk: Current trends and future pathways L. Reimann et al. 10.1017/cft.2023.3
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74. Experimentelle georeferenzierte Bevölkerungszahl auf Basis der Bevölkerungsfortschreibung und Mobilfunkdaten S. Hadam 10.1007/s11943-023-00320-2
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77. Spatial access inequities and childhood immunisation uptake in Kenya N. Joseph et al. 10.1186/s12889-020-09486-8
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81. Global Harmonization of Urbanization Measures: Proceed with Care D. Balk et al. 10.3390/rs13244973
82. A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015 M. Archila Bustos et al. 10.1007/s11111-020-00360-8
83. Climatic Criteria of the Need for Preventive Adaptation A. Ginzburg et al. 10.1134/S0001433822060068
84. Impact of state weights on national vaccination coverage estimates from household surveys in Nigeria T. Dong et al. 10.1016/j.vaccine.2020.05.026
85. Stability and Changes in the Spatial Distribution of China’s Population in the Past 30 Years Based on Census Data Spatialization X. Xu et al. 10.3390/rs15061674
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87. Remote sensing of urban greenspace exposure and equality: Scaling effects from greenspace and population mapping S. Wu et al. 10.1016/j.ufug.2023.128136
88. High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa H. Chamberlain et al. 10.1038/s41597-022-01799-0
89. Integrating remote sensing with OpenStreetMap data for comprehensive scene understanding through multi-modal self-supervised learning L. Bai et al. 10.1016/j.rse.2024.114573
90. A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas C. He et al. 10.1007/s13753-021-00385-z
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94. Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model O. Yankey et al. 10.1016/j.apgeog.2024.103416
95. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya D. Thomson et al. 10.3390/urbansci5020048
96. Human and infrastructure exposure to large wildfires in the United States A. Modaresi Rad et al. 10.1038/s41893-023-01163-z
97. Evaluation of NLDAS-2 and Downscaled Air Temperature data in Florida J. Jung et al. 10.1080/02723646.2021.1928878
98. Spatially explicit reconstruction of the population distribution in the Tuojiang River Basin during 1911–2010 using random forest regression Q. Wang et al. 10.1007/s10113-021-01872-1
99. A simulated ‘sandbox’ for exploring the modifiable areal unit problem in aggregation and disaggregation J. Nieves et al. 10.1038/s41597-024-03061-1
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103. The Unseen Population: Underestimation of Slum Dwellers in Cities of the Global South J. Breuer et al. 10.2139/ssrn.4098362
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108. The Ratio of the Land Consumption Rate to the Population Growth Rate: A Framework for the Achievement of the Spatiotemporal Pattern in Poland and Lithuania B. Calka et al. 10.3390/rs14051074
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110. Elucidating the impacts of rapid urban expansion on air quality in the Yangtze River Delta, China X. Zhang et al. 10.1016/j.scitotenv.2021.149426
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144. The unseen population: Do we underestimate slum dwellers in cities of the Global South? J. Breuer et al. 10.1016/j.habitatint.2024.103056
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