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14. Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach M. Kuffer et al. 10.3390/urbansci5040072
15. Intercensal Updating Using Structure-Preserving Methods and Satellite Imagery T. Koebe et al. 10.1111/rssa.12802
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18. Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways L. Reimann et al. 10.1088/1748-9326/ac0b66
19. Estimating Chinese residential populations from analysis of impervious surfaces derived from satellite images S. Wei et al. 10.1080/01431161.2020.1841322
20. Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa F. Hierink et al. 10.1038/s43856-022-00179-4
21. Multi-Resolution Population Mapping Based on a Stepwise Downscaling Approach Using Multisource Data Y. Jin et al. 10.3390/rs15071947
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26. Optimized spatial information for 1990, 2000, and 2010 U.S. census microdata C. Fowler et al. 10.1038/s41597-023-02859-9
27. Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa M. Owusu et al. 10.3390/urbansci7040116
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29. A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies S. Lindersson et al. 10.1002/wat2.1424
30. The impact of flooding on food security across Africa C. Reed et al. 10.1073/pnas.2119399119
31. U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil F. Wagner et al. 10.3390/rs12101544
32. Increasing Spatial Mismatch of Cropland-Grain Production-Population in China over the Past Two Decades L. Li et al. 10.3390/land11101685
33. Current and future rainfall-driven flood risk from hurricanes in Puerto Rico under 1.5 and 2 °C climate change L. Archer et al. 10.5194/nhess-24-375-2024
34. Improving access to care and community health in Haiti with optimized community health worker placement C. Champagne et al. 10.1371/journal.pgph.0000167
35. Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea B. Fries et al. 10.1371/journal.pone.0248646
36. Social vulnerability of the people exposed to wildfires in U.S. West Coast states A. Modaresi Rad et al. 10.1126/sciadv.adh4615
37. Estimating the Population of a Middle Eastern City Based on a Normalized Difference Built-Up Index and Urban Morphology E. Alyasiri et al. 10.4018/IJAGR.313942
38. Societal determinants of flood-induced displacement J. Vestby et al. 10.1073/pnas.2206188120
39. Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000 F. Wang et al. 10.3390/su12031231
40. Dealing with uncertainty in famine predictions: How complex events affect food security early warning skill in the Greater Horn of Africa P. Krishnamurthy et al. 10.1016/j.gfs.2020.100374
41. A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints X. Huang et al. 10.1080/20964471.2020.1776200
42. Flood hazard potential reveals global floodplain settlement patterns L. Devitt et al. 10.1038/s41467-023-38297-9
43. So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale S. Doda et al. 10.1038/s41597-022-01780-x
44. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations J. Nieves et al. 10.3390/rs12101545
45. How much time have we got? K. Kausrud et al. 10.3897/aca.4.e68934
46. Occupations on the map: Using a super learner algorithm to downscale labor statistics M. van Dijk et al. 10.1371/journal.pone.0278120
47. Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases M. Evans et al. 10.1038/s41598-023-48390-0
48. Small area population denominators for improved disease surveillance and response A. Tatem 10.1016/j.epidem.2022.100597
49. Spatiotemporal dynamics of global population and heat exposure (2020–2100): based on improved SSP-consistent population projections M. Li et al. 10.1088/1748-9326/ac8755
50. The unseen population: Do we underestimate slum dwellers in cities of the Global South? J. Breuer et al. 10.1016/j.habitatint.2024.103056
51. Human populations in the world’s mountains: Spatio-temporal patterns and potential controls J. Thornton et al. 10.1371/journal.pone.0271466
52. A novel GIS-based multicriteria analysis approach for ascertaining the catchment-scale degradation of a Himalayan wetland I. Rashid et al. 10.1016/j.envres.2023.115967
53. 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
54. 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
55. 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
56. First year with COVID-19: Assessment and prospects R. Bergquist et al. 10.4081/gh.2020.953
57. Modeling climate migration: dead ends and new avenues R. Beyer et al. 10.3389/fclim.2023.1212649
58. 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
59. Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. 10.1111/jfr3.12952
60. Satellite imaging reveals increased proportion of population exposed to floods B. Tellman et al. 10.1038/s41586-021-03695-w
61. Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example C. Liu et al. 10.3390/rs15112926
62. EO4Migration: The Design of an EO-Based Solution in Support of Migrants’ Inclusion and Social-Cohesion Policies M. Aquilino et al. 10.3390/rs14174295
63. 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
64. Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling D. Palacios-Lopez et al. 10.3390/rs14020325
65. Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach L. Han et al. 10.3390/rs14194985
66. Role of Chinese cities in abating aviation carbon emissions based on gridded population data and power law model F. Li et al. 10.1016/j.energy.2023.129762
67. La population vue du ciel : quand l’imagerie satellite vient au secours du recensement E. Darin et al. 10.3917/popu.2203.0467
68. Uncovering temporal changes in Europe’s population density patterns using a data fusion approach F. Batista e Silva et al. 10.1038/s41467-020-18344-5
69. Violent conflict exacerbated drought-related food insecurity between 2009 and 2019 in sub-Saharan Africa W. Anderson et al. 10.1038/s43016-021-00327-4
70. A review of climate security risk assessment tools B. Šedová et al. 10.1177/27538796241226996
71. Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States J. Uhl et al. 10.5194/essd-13-119-2021
72. Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda J. Van Den Hoek & H. Friedrich 10.3390/rs13183574
73. Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany S. Eichhorn 10.3390/su12103976
74. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs) D. Thomson et al. 10.3390/socsci9050080
75. Looking Back, Looking Forward: Progress and Prospect for Spatial Demography S. Matthews et al. 10.1007/s40980-021-00084-9
76. Urban ecosystem services supply-demand assessment from the perspective of the water-energy-food nexus T. Ding et al. 10.1016/j.scs.2023.104401
77. Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China X. Zhao et al. 10.1080/15481603.2021.1935128
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79. Assessing the relative accuracy of gridded population sampling: results from an election survey experiment S. Ford & M. Kirwin 10.1080/13645579.2022.2091200
80. Downscaling population and urban land use for socio-economic scenarios in the UK M. Merkle et al. 10.1007/s10113-022-01963-7
81. High-resolution gridded estimates of population sociodemographics from the 2020 census in California N. Depsky et al. 10.1371/journal.pone.0270746
82. GHS-POP Accuracy Assessment: Poland and Portugal Case Study B. Calka & E. Bielecka 10.3390/rs12071105
83. 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
84. Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda D. Thomson et al. 10.1186/s12942-020-00230-4
85. High-resolution population estimation using household survey data and building footprints G. Boo et al. 10.1038/s41467-022-29094-x
86. Mapping Changing Population Distribution on the Qinghai–Tibet Plateau since 2000 with Multi-Temporal Remote Sensing and Point-of-Interest Data L. Li et al. 10.3390/rs12244059
87. Exploring methods for mapping seasonal population changes using mobile phone data D. Woods et al. 10.1057/s41599-022-01256-8
88. Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning H. Zhuang et al. 10.3390/rs13173533
89. The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets M. Kuffer et al. 10.3390/ijgi11070403
90. 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
91. A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India C. Pezzulo et al. 10.1038/s41597-023-01961-2
92. Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications D. Ehrlich et al. 10.3390/su13147851
93. Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale M. Aquilino et al. 10.3390/rs13142835
94. Gridded reconstruction of the population in the traditional cultivated region of China from 1776 to 1953 X. Zhang et al. 10.1007/s11430-020-9866-2
95. Different Spatiotemporal Patterns in Global Human Population and Built‐Up Land J. Gao & B. O'Neill 10.1029/2020EF001920
96. 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
97. Delimitation of rural and urban areas from spatial data in statistical grids: the province of Jaén (Spain) as a case study J. Sánchez-Martínez & A. Garrido-Almonacid 10.1080/17445647.2024.2301980
98. Dynamics of population growth in secondary cities across southern Africa A. Zimmer et al. 10.1007/s10980-020-01086-6
99. Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics R. Chen et al. 10.3390/ijgi9110637
100. Role of transportation network on population distribution evolution F. Zhao & H. Shang 10.1016/j.physa.2021.126076
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102. Global population profile of tropical cyclone exposure from 2002 to 2019 R. Jing et al. 10.1038/s41586-023-06963-z
103. Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method* W. Guo et al. 10.1080/17538947.2023.2233493
104. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic C. Smith & J. Mennis 10.5888/pcd17.200246
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106. Mapping population on Tibetan Plateau by fusing VIIRS data and nighttime Tencent location-based services data X. Ma et al. 10.1016/j.ecolind.2022.108893
107. Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA) X. Yin et al. 10.3390/ijgi10100681
108. Towards a new spatial representation of faecal sources and pathways in unsewered urban catchments using open-source data M. Sultana et al. 10.2166/washdev.2023.227
109. 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
110. 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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112. Experimentelle georeferenzierte Bevölkerungszahl auf Basis der Bevölkerungsfortschreibung und Mobilfunkdaten S. Hadam 10.1007/s11943-023-00320-2
113. Mapping Gridded Gross Domestic Product Distribution of China Using Deep Learning With Multiple Geospatial Big Data Y. Chen et al. 10.1109/JSTARS.2022.3148448
114. Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates A. Pozzer et al. 10.1029/2022GH000711
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116. Mapping Human Activity Volumes Through Remote Sensing Imagery X. Xing et al. 10.1109/JSTARS.2020.3023730
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118. Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas J. Baynes et al. 10.5194/essd-14-2833-2022
119. High-resolution gridded population datasets for Latin America and the Caribbean using official statistics T. McKeen et al. 10.1038/s41597-023-02305-w
120. Spatial access inequities and childhood immunisation uptake in Kenya N. Joseph et al. 10.1186/s12889-020-09486-8
121. Assessing OSM building completeness for almost 13,000 cities globally Q. Zhou et al. 10.1080/17538947.2022.2159550
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124. Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps? S. Lindersson et al. 10.5194/nhess-21-2921-2021
125. Global Harmonization of Urbanization Measures: Proceed with Care D. Balk et al. 10.3390/rs13244973
126. 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
127. Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan S. Wang et al. 10.3390/fi13100251
128. Climatic Criteria of the Need for Preventive Adaptation A. Ginzburg et al. 10.1134/S0001433822060068
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130. 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
131. Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems H. He et al. 10.3390/su151612247
132. 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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135. 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
136. 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
137. A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas C. He et al. 10.1007/s13753-021-00385-z
138. Global trends and local variations in land take per person M. Li et al. 10.1016/j.landurbplan.2021.104308
139. Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa B. Khavari et al. 10.1038/s41597-021-00897-9
140. A grid-based sample design framework for household surveys G. Boo et al. 10.12688/gatesopenres.13107.1
141. A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators K. Nilsen et al. 10.1186/s12913-021-06370-y
142. Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method W. Guo et al. 10.1109/JSTARS.2023.3238188
143. Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data C. Jing et al. 10.3390/rs12193235
144. The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries M. Kuffer et al. 10.3390/rs12060982
145. Fire Danger Observed from Space M. Pettinari & E. Chuvieco 10.1007/s10712-020-09610-8
146. Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates F. Schug et al. 10.1371/journal.pone.0249044
147. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya D. Thomson et al. 10.3390/urbansci5020048
148. Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data M. Schiavina et al. 10.3390/su11205674
149. Human and infrastructure exposure to large wildfires in the United States A. Modaresi Rad et al. 10.1038/s41893-023-01163-z
150. Evaluation of NLDAS-2 and Downscaled Air Temperature data in Florida J. Jung et al. 10.1080/02723646.2021.1928878
151. Empiric recommendations for population disaggregation under different data scenarios M. Sapena et al. 10.1371/journal.pone.0274504
152. 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
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154. A simulated ‘sandbox’ for exploring the modifiable areal unit problem in aggregation and disaggregation J. Nieves et al. 10.1038/s41597-024-03061-1
155. Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets M. Mohanty & S. Simonovic 10.1016/j.scitotenv.2020.143559
156. Implications for Tracking SDG Indicator Metrics with Gridded Population Data C. Tuholske et al. 10.3390/su13137329
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161. How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia D. Thomson et al. 10.1371/journal.pone.0271504
162. The Unseen Population: Underestimation of Slum Dwellers in Cities of the Global South J. Breuer et al. 10.2139/ssrn.4098362
163. Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery Y. Tang et al. 10.3390/rs13101900
164. Cropland abandonment in the context of drought, economic restructuring, and migration in northeast Syria L. Eklund et al. 10.1088/1748-9326/ad1723
165. Global flood exposure from different sized rivers M. Bernhofen et al. 10.5194/nhess-21-2829-2021
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167. Measuring the contribution of built-settlement data to global population mapping J. Nieves et al. 10.1016/j.ssaho.2020.100102
168. 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
169. Two centuries of settlement and urban development in the United States S. Leyk et al. 10.1126/sciadv.aba2937
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171. Assessment of observed changes in drought characteristics and recent vegetation dynamics over arid and semiarid areas in Sudan M. Alriah et al. 10.1007/s00704-023-04824-3
172. 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
173. Fine-scale population spatialization data of China in 2018 based on real location-based big data M. Chen et al. 10.1038/s41597-022-01740-5
174. Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China G. Qiu et al. 10.3390/rs12101618
175. Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling Y. Mei et al. 10.1080/10095020.2021.2021785
176. Technology transfer for sustainable rural development: evidence from homestead withdrawal with compensation in Chengdu–Chongqing T. Xia et al. 10.1007/s10961-023-10019-9
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178. Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges S. Koutra & C. Ioakimidis 10.3390/land12010083
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180. Development and implementation of automatic metadata generation framework for SDI using OSS: a case study of Indian NSDI S. Agrawal et al. 10.1007/s12517-022-09635-w
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