90 citations as recorded by crossref.

1. The utility of built environment geospatial data for high-resolution dasymetric global population modeling S. Rubinyi et al. 10.1016/j.compenvurbsys.2021.101594
2. Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale M. Aquilino et al. 10.3390/rs13142835
3. 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
4. Different Spatiotemporal Patterns in Global Human Population and Built‐Up Land J. Gao & B. O'Neill 10.1029/2020EF001920
5. Identifying vulnerable populations and transmission pathways by geographic correlation of the environment to human health T. Price & S. Barua 10.1016/j.scitotenv.2021.146426
6. Combining Conventional Statistics and Big Data to Map Global Tourism Destinations Before COVID-19 C. Adamiak & B. Szyda 10.1177/00472875211051418
7. 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
8. Dynamics of population growth in secondary cities across southern Africa A. Zimmer et al. 10.1007/s10980-020-01086-6
9. Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics R. Chen et al. 10.3390/ijgi9110637
10. Role of transportation network on population distribution evolution F. Zhao & H. Shang 10.1016/j.physa.2021.126076
11. Comparative assessment of gridded population data sets for complex topography: a study of Southwest China Y. Xu et al. 10.1007/s11111-020-00366-2
12. Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter K. MacManus et al. 10.5194/essd-13-5747-2021
13. Asset exposure data for global physical risk assessment S. Eberenz et al. 10.5194/essd-12-817-2020
14. Monitoring and projecting global hunger: Are we on track? M. Cooper et al. 10.1016/j.gfs.2021.100568
15. 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
16. Assessing OSM building completeness using population data Y. Zhang et al. 10.1080/13658816.2021.2023158
17. Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic C. Smith & J. Mennis 10.5888/pcd17.200246
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. Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA) X. Yin et al. 10.3390/ijgi10100681
21. An introduction to DUIA: The database on urban inequality and amenities F. Ramos et al. 10.1371/journal.pone.0253824
22. Improving the accuracy of extant gridded population maps using multisource map fusion P. Gao et al. 10.1080/15481603.2021.2012371
23. High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent D. Palacios-Lopez et al. 10.3390/rs13061142
24. 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
25. 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
26. Mapping Human Activity Volumes Through Remote Sensing Imagery X. Xing et al. 10.1109/JSTARS.2020.3023730
27. Using satellite imagery to understand and promote sustainable development M. Burke et al. 10.1126/science.abe8628
28. 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
29. Spatial access inequities and childhood immunisation uptake in Kenya N. Joseph et al. 10.1186/s12889-020-09486-8
30. 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
31. Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps? S. Lindersson et al. 10.5194/nhess-21-2921-2021
32. Global Harmonization of Urbanization Measures: Proceed with Care D. Balk et al. 10.3390/rs13244973
33. 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
34. Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan S. Wang et al. 10.3390/fi13100251
35. 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
36. 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
37. Mapping monthly population distribution and variation at 1-km resolution across China Z. Cheng et al. 10.1080/13658816.2020.1854767
38. 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
39. 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
40. Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations J. Nieves et al. 10.3390/rs12101545
41. A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas C. He et al. 10.1007/s13753-021-00385-z
42. How much time have we got? K. Kausrud et al. 10.3897/aca.4.e68934
43. Global trends and local variations in land take per person M. Li et al. 10.1016/j.landurbplan.2021.104308
44. 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
45. A grid-based sample design framework for household surveys G. Boo et al. 10.12688/gatesopenres.13107.1
46. 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
47. Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data C. Jing et al. 10.3390/rs12193235
48. 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
49. 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
50. Fire Danger Observed from Space M. Pettinari & E. Chuvieco 10.1007/s10712-020-09610-8
51. 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
52. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya D. Thomson et al. 10.3390/urbansci5020048
53. 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
54. 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
55. First year with COVID-19: Assessment and prospects R. Bergquist et al. 10.4081/gh.2020.953
56. Evaluation of NLDAS-2 and Downscaled Air Temperature data in Florida J. Jung et al. 10.1080/02723646.2021.1928878
57. Satellite imaging reveals increased proportion of population exposed to floods B. Tellman et al. 10.1038/s41586-021-03695-w
58. 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
59. Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets M. Mohanty & S. Simonovic 10.1016/j.scitotenv.2020.143559
60. Implications for Tracking SDG Indicator Metrics with Gridded Population Data C. Tuholske et al. 10.3390/su13137329
61. 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
62. 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
63. The uncertainty of flood frequency analyses in hydrodynamic model simulations X. Zhou et al. 10.5194/nhess-21-1071-2021
64. 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
65. Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States J. Uhl et al. 10.5194/essd-13-119-2021
66. 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
67. Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany S. Eichhorn 10.3390/su12103976
68. Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery Y. Tang et al. 10.3390/rs13101900
69. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs) D. Thomson et al. 10.3390/socsci9050080
70. Looking Back, Looking Forward: Progress and Prospect for Spatial Demography S. Matthews et al. 10.1007/s40980-021-00084-9
71. 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
72. Global flood exposure from different sized rivers M. Bernhofen et al. 10.5194/nhess-21-2829-2021
73. Disaggregating population data for assessing progress of SDGs: methods and applications Y. Qiu et al. 10.1080/17538947.2021.2013553
74. Measuring the contribution of built-settlement data to global population mapping J. Nieves et al. 10.1016/j.ssaho.2020.100102
75. 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
76. Two centuries of settlement and urban development in the United States S. Leyk et al. 10.1126/sciadv.aba2937
77. GHS-POP Accuracy Assessment: Poland and Portugal Case Study B. Calka & E. Bielecka 10.3390/rs12071105
78. 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
79. 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
80. High-resolution population estimation using household survey data and building footprints G. Boo et al. 10.1038/s41467-022-29094-x
81. 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
82. 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
83. Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning H. Zhuang et al. 10.3390/rs13173533
84. 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
85. Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling Y. Mei et al. 10.1080/10095020.2021.2021785
86. Emergency flood bulletins for Cyclones Idai and Kenneth: A critical evaluation of the use of global flood forecasts for international humanitarian preparedness and response R. Emerton et al. 10.1016/j.ijdrr.2020.101811
87. 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
88. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos A. Gaughan et al. 10.1088/2515-7620/ab3d91
89. 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
90. People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data T. Kugler et al. 10.1007/s11111-019-00326-5