Breaking new ground in mapping human settlements from space – The Global Urban Footprint

Power-Law Distributions in Empirical Data

Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019

Global food demand and the sustainable intensification of agriculture

Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116437108 · Cited by 7,692

Global Change and the Ecology of Cities

Grimm, N., Faeth, S., Golubiewski, N., Redman, C., Wu, J., Bai, X.-M., & Briggs, J. (2008) · Science · doi:10.1126/science.1150195 · Cited by 6,789

Power-law distributions in empirical data

Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,710

World Urbanization Prospects: The 1996 Revision

Brockerhoff, M. P., & Nations, U. (1998) · Population and Development Review · doi:10.2307/2808041 · Cited by 5,900

A Meta-Analysis of Global Urban Land Expansion

Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023777 · Cited by 2,406

Global land cover mapping at 30 m resolution: A POK-based operational approach

Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Shu, P., Lu, M., et al. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.09.002 · Cited by 2,120

World Urbanization Prospects: an alternative to the UN model of projection compatible with the mobility transition theory

Bocquier, P. (2005) · Demographic Research · doi:10.4054/demres.2005.12.9 · Cited by 1,746

TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry

Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M. U., Younis, M., & Zink, M. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.900693 · Cited by 1,690

Human behavior and the principle of least effort.

indicated, N. A. (1949) · Journal of Consulting Psychology · doi:10.1037/h0052803 · Cited by 1,610

Space use by animals on the urban fringe: interactive effects of sex and personality

Wat, K. K. Y., Herath, A. P. H. M., Rus, A. I., Banks, P. B., & McArthur, C. (2019) · Behavioral Ecology · doi:10.1093/beheco/arz194 · Cited by 49

Towards a Large-Scale 3D Modeling of the Built Environment—Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

Esch, T., Zeidler, J., Palacios‐Lopez, D., Marconcini, M., Roth, A., Mönks, M., Leutner, B. F., Brzoska, E., Metz-Marconcini, A., Bachofer, F., et al. (2020) · Remote Sensing · doi:10.3390/rs12152391 · Cited by 49

Urban signals in high-resolution weather and climate simulations: role of urban land-surface characterisation

Hertwig, D., Grimmond, S., Hendry, M. A., Saunders, B., Wang, Z., Jeoffrion, M., Vidale, P. L., McGuire, P. C., Bohnenstengel, S. I., Ward, H. C., et al. (2020) · Theoretical and Applied Climatology · doi:10.1007/s00704-020-03294-1 · Cited by 49

Summer average urban-rural surface temperature differences do not indicate the need for urban heat reductionPreprint

Martilli, A., Roth, M., Chow, W., Demuzere, M., Lipson, M. J., Krayenhoff, E. S., Sailor, D. J., Nazarian, N., Voogt, J., Wouters, H., et al. (2020) · doi:10.31219/osf.io/8gnbf · Cited by 48

Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data

Geiß, C., Schrade, H., Pelizari, P. A., & Taubenböck, H. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.10.004 · Cited by 51

GABLE: A first fine-grained 3D building model of China on a national scale from very high resolution satellite imagery

Sun, X., Huang, X., Mao, Y., Sheng, T., Li, J., Wang, Z., Lu, X. M., Ma, X., Tang, D., & Chen, K. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114057 · Cited by 47

Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions—A Review

Kuenzer, C., Heimhuber, V., Huth, J., & Dech, S. W. (2019) · Remote Sensing · doi:10.3390/rs11171985 · Cited by 47

Global urban and rural settlement dataset from 2000 to 2020

Liu, Z., Huang, S., Fang, C., Guan, L., & Liu, M. (2024) · Scientific Data · doi:10.1038/s41597-024-04195-y · Cited by 47

FSRSS-Net: High-Resolution Mapping of Buildings from Middle-Resolution Satellite Images Using a Super-Resolution Semantic Segmentation Network

Zhang, T., Tang, H., Ding, Y., Li, P., Ji, C., & Xu, P. (2021) · Remote Sensing · doi:10.3390/rs13122290 · Cited by 46

Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping

Zhao, J., Pelich, R., Hostache, R., Matgen, P., Cao, S., Wagner, W., & Chini, M. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112668 · Cited by 52

Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review

Dyer, G. M. C., Khomenko, S., Adlakha, D., Anenberg, S. C., Behnisch, M., Boeing, G., Esperón‐Rodríguez, M., Gasparrini, A., Khreis, H., Kondo, M. C., et al. (2024) · Environmental Research · doi:10.1016/j.envres.2024.119324 · Cited by 49

COVID-19 in Toronto: A Spatial Exploratory Analysis

Vaz, E. (2021) · Sustainability · doi:10.3390/su13020498 · Cited by 44

Fire Danger Observed from Space

Pettinari, M. L., & Chuvieco, E. (2020) · Surveys in Geophysics · doi:10.1007/s10712-020-09610-8 · Cited by 54

Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach

Ghazaryan, G., Rienow, A., Oldenburg, C., Thonfeld, F., Trampnau, B., Sticksel, S., & Jürgens, C. (2021) · Remote Sensing · doi:10.3390/rs13091694 · Cited by 43

Modeling Urbanization Patterns with Generative Adversarial Networks

Albert, A., Strano, E., Kaur, J., & González, M. C. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518032 · Cited by 41

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles

Kulinkina, A. V., Walz, Y., Koch, M., Biritwum, N., Utzinger, J., & Naumova, E. N. (2018) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0006517 · Cited by 41

Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States (Haiti, Dominica and St. Lucia)

Goldblatt, R., Jones, N. K. W., & Mannix, J. (2020) · Remote Sensing · doi:10.3390/rs12010118 · Cited by 41

Measuring and Monitoring Urban Impacts on Climate Change from Space

MILESI, C., & Churkina, G. (2020) · Remote Sensing · doi:10.3390/rs12213494 · Cited by 41

Building footprint data for countries in Africa: To what extent are existing data products comparable?

Chamberlain, H. R., Darin, É., Adewole, W. A., Jochem, W. C., Lázár, A. N., & Tatem, A. J. (2024) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2024.102104 · Cited by 40

Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A review of characteristics, approaches, and datasets

Zhao, J., Li, M., Li, Y., Matgen, P., & Chini, M. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3496075 · Cited by 40

Exploiting big earth data from space – first experiences with the timescan processing chain

Esch, T., Üreyen, S., Zeidler, J., Metz-Marconcini, A., Hirner, A., Asamer, H., Tum, M., Böttcher, M., Kuchař, Š., Svatoň, V., et al. (2018) · Big Earth Data · doi:10.1080/20964471.2018.1433790 · Cited by 44

CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring

Iino, S., Ito, R., Doi, K., Imaizumi, T., & Hikosaka, S. (2018) · International Journal of Image and Data Fusion · doi:10.1080/19479832.2018.1491897 · Cited by 40

Automated Extraction of Built-Up Areas by Fusing VIIRS Nighttime Lights and Landsat-8 Data

Liu, C., Yang, K., Bennett, M. M., Guo, Z., Cheng, L., & Li, M. (2019) · Remote Sensing · doi:10.3390/rs11131571 · Cited by 40

Urban morphology parameters from global digital elevation models: Implications for aerodynamic roughness and for wind-speed estimation

Kent, C. W., Grimmond, S., Gatey, D., & Hirano, K. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.09.024 · Cited by 39

Different effects of human settlements changes on landscape fragmentation in China: Evidence from grid cell

Li, G., Fang, C., & Qi, W. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107927 · Cited by 40

A global North-South division line for portraying urban development

Zhang, Y., Li, X., Wang, S., Yao, Y., Li, Q., Tu, W., Zhao, H., Zhao, H., Feng, K., Sun, L., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102729 · Cited by 38

Mapping Trends in Insecticide Resistance Phenotypes in African Malaria VectorsPreprint

Hancock, P. A., Hendriks, C. M. J., Tangena, J.-A. A., Gibson, H. S., Hemingway, J., Coleman, M., Gething, P. W., Cameron, E., Bhatt, S., & Moyes, C. L. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.06.895656 · Cited by 39

Integrating Environmental Context into DHS Analysis While Protecting Participant Confidentiality: A New Remote Sensing Method

Grace, K. L., Nagle, N. N., Burgert‐Brucker, C. R., Rutzick, S., Riper, D. C. V., Dontamsetti, T., & Croft, T. N. (2018) · Population and Development Review · doi:10.1111/padr.12222 · Cited by 37

The changing landscape of Plasmodium falciparum drug resistance in the Democratic Republic of Congo

Deutsch-Feldman, M., Aydemir, Ö., Carrel, M., Brazeau, N. F., Bhatt, S., Bailey, J. A., Kashamuka, M. M., Tshefu, A. K., Taylor, S. M., Juliano, J. J., et al. (2019) · BMC Infectious Diseases · doi:10.1186/s12879-019-4523-0 · Cited by 36

Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries

Li, W., El-Askary, H. M., Lakshmi, V., Piechota, T. C., & Struppa, D. C. (2020) · Remote Sensing · doi:10.3390/rs12091391 · Cited by 36

High‐resolution global climate simulations: Representation of cities

Hertwig, D., Ng, M., Grimmond, S., Vidale, P. L., & McGuire, P. C. (2021) · International Journal of Climatology · doi:10.1002/joc.7018 · Cited by 36

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

Całka, B., Orych, A., Bielecka, E., & Mozūriūnaitė, S. (2022) · Remote Sensing · doi:10.3390/rs14051074 · Cited by 36

Monitoring long-term shoreline dynamics and human activities in the Hangzhou Bay, China, combining daytime and nighttime EO data

Chu, L., Oloo, F., Sudmanns, M., Dirk Tiede, A., Hölbling, D. D., Blaschke, T., & Teleoaca, I. (2020) · Big Earth Data · doi:10.1080/20964471.2020.1740491 · Cited by 36

Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African mosquito malaria vector species

Hancock, P. A., Lynd, A., Wiebe, A., Devine, M., Essandoh, J., Wat’senga, F., Manzambi, E. Z., Agossa, F. R., Donnelly, M. J., Weetman, D., et al. (2022) · BMC Biology · doi:10.1186/s12915-022-01242-1 · Cited by 30

Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge

Esch, T., Asamer, H., Bachofer, F., Balhar, J., Boettcher, M., Boissier, E., d’Angelo, P., Gevaert, C. M., Hirner, A., Jupova, K., et al. (2018) · International Journal of Digital Earth · doi:10.1080/17538947.2018.1548655 · Cited by 35

Prediction of Socio-Economic Indicators for Urban Planning Using VHR Satellite Imagery and Spatial Analysis

Warth, G., Braun, A., Assmann, O., Fleckenstein, K., & Hochschild, V. (2020) · Remote Sensing · doi:10.3390/rs12111730 · Cited by 36

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

Hoek, J. V. D., & Friedrich, H. K. (2021) · Remote Sensing · doi:10.3390/rs13183574 · Cited by 34

A Long-Term Archaeological Reappraisal of Low-Density Urbanism: Implications for Contemporary Cities

Hawken, S., & Fletcher, R. J. (2021) · Journal of Urban Archaeology · doi:10.1484/j.jua.5.123674 · Cited by 33

Open government geospatial data on buildings for planning sustainable and resilient citiesPreprint

Biljecki, F., Chew, L. Z. X., Milojevic-Dupont, N., & Creutzig, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04023 · Cited by 33

Built-up area mapping using Sentinel-1 SAR data

Verma, A., Bhattacharya, A., Dey, S., López-Martínez, C., & Gamba, P. E. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.07.019 · Cited by 32

Identifying urban areas by combining human judgment and machine learning: An application to India

Galdo, V., Li, Y., & Rama, M. (2019) · Journal of Urban Economics · doi:10.1016/j.jue.2019.103229 · Cited by 34

Tropical or extratropical cyclones: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?

Nederhoff, K., Leijnse, T., Parker, K. A., Thomas, J., O'Neill, A. C., van Ormondt, M., McCall, R., Erikson, L., Barnard, P. L., Foxgrover, A. C., et al. (2024) · Natural Hazards · doi:10.1007/s11069-024-06552-x · Cited by 41

Global Variation in Climate, Human Development, and Population Density Has Implications for Urban Ecosystem Services

Richards, D. R., Masoudi, M., Oh, R. R. Y., Yando, E. S., Zhang, J., Friess, D. A., Grêt‐Regamey, A., Tan, P. Y., & Edwards, P. J. (2019) · Sustainability · doi:10.3390/su11226200 · Cited by 33

Mapping of Maximum and Minimum Inundation Extents in the Amazon Basin 2014–2017 with ALOS-2 PALSAR-2 ScanSAR Time-Series Data

Rosenqvist, J., Rosenqvist, Å., Jensen, K., & McDonald, K. C. (2020) · Remote Sensing · doi:10.3390/rs12081326 · Cited by 31

High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent

Palacios‐Lopez, D., Bachofer, F., Esch, T., Marconcini, M., MacManus, K., Sorichetta, A., Zeidler, J., Dech, S. W., Tatem, A. J., & Reinartz, P. H. (2021) · Remote Sensing · doi:10.3390/rs13061142 · Cited by 30

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

Braun, A., Warth, G., Bachofer, F., Schultz, M., & Hochschild, V. (2023) · Land · doi:10.3390/land12101885 · Cited by 29

Intra-Urban Change Analysis Using Sentinel-1 and Nighttime Light Data

Che, M., & Gamba, P. E. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2899881 · Cited by 28

Accurately monitoring urbanization at global scale – the world settlement footprint

Marconcini, M., Gorelick, N., Metz-Marconcini, A., & Esch, T. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/509/1/012036 · Cited by 28

CORONA High-Resolution Satellite and Aerial Imagery for Change Detection Assessment of Natural Hazard Risk and Urban Growth in El Alto/La Paz in Bolivia, Santiago de Chile, Yungay in Peru, Qazvin in Iran, and Mount St. Helens in the USA

Fekete, A. (2020) · Remote Sensing · doi:10.3390/rs12193246 · Cited by 28

Greenness and excess deaths from heat in 323 Latin American cities: Do associations vary according to climate zone or green space configuration?

Schinasi, L. H., Bakhtsiyarava, M., Sánchez, B. N., Kephart, J. L., Ju, Y., Arunachalam, S., Gouveia, N., Caiaffa, W. T., O’Neill, M. S., Dronova, I., et al. (2023) · Environment International · doi:10.1016/j.envint.2023.108230 · Cited by 30

Application of training data affects success in broad-scale local climate zone mapping

Xu, C., Hystad, P., Chen, R., Hoek, J. V. D., Hutchinson, R., Hankey, S., & Kennedy, R. E. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102482 · Cited by 27

Collaborative Extraction of Paddy Planting Areas with Multi-Source Information Based on Google Earth Engine: A Case Study of Cambodia

Kang, J., Yang, X., Wang, Z., Huang, C., & Wang, J. (2022) · Remote Sensing · doi:10.3390/rs14081823 · Cited by 27

Recent advances in carbon footprint studies of urban ecosystems: overview, application, and future challenges

Chen, K., Yang, M., Zhou, X., Liu, Z., Li, P., Tang, J., & Peng, C. H. (2022) · Environmental Reviews · doi:10.1139/er-2021-0111 · Cited by 30

Estimating urban population patterns from stereo-satellite imagery

Steinnocher, K., Bono, A., Chatenoux, B., Dirk Tiede, A., & Wendt, L. (2019) · European Journal of Remote Sensing · doi:10.1080/22797254.2019.1604081 · Cited by 25

On the knowledge gain of urban morphology from space

Wang, J., Georganos, S., Kuffer, M., Abascal, Á., & Vanhuysse, S. G. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101831 · Cited by 25

Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs

VARQUEZ, A. C. G., Dong, S., Hanaoka, S., & Kanda, M. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104442 · Cited by 25

Automatic Delineation of Urban Growth Boundaries Based on Topographic Data Using Germany as a Case Study

Harig, O., Hecht, R., Burghardt, D., & Meinel, G. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10050353 · Cited by 26

Automatic mapping afforestation, cropland reclamation and variations in cropping intensity in central east China during 2001–2016

Qiu, B., Zou, F., Chen, C., Tang, Z., Zhong, J., & Yan, X. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.04.010 · Cited by 24

Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa

Hillson, R., Coates, A., Alejandre, J. D., Jacobsen, K. H., Ansumana, R., Bockarie, A. S., Bangura, U., Lamin, J. M., & Stenger, D. A. (2019) · International Journal of Health Geographics · doi:10.1186/s12942-019-0180-1 · Cited by 23

Land Consumption Monitoring with SAR Data and Multispectral Indices

Luti, T., Fioravante, P. D., Marinosci, I., Strollo, A., Riitano, N., Falanga, V., Mariani, L., Congedo, L., & Munafò, M. (2021) · Remote Sensing · doi:10.3390/rs13081586 · Cited by 23

The agglomeration and dispersion dichotomy of human settlements on Earth

Strano, E., Simini, F., Nadai, M. D., Esch, T., & Marconcini, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-02743-9 · Cited by 28

Shifts in Forest Species Composition and Abundance under Climate Change Scenarios in Southern Carpathian Romanian Temperate Forests

García-Duro, J., Ciceu, A., Chivulescu, Ş., Badea, O. N., Tanase, M. A., & Aponte, C. (2021) · Forests · doi:10.3390/f12111434 · Cited by 23

Assessing the suitability of urban-oriented land cover products for mapping rural settlements

Kaim, D., Ziółkowska, E., Grădinaru, S. R., & Pazúr, R. (2022) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2022.2075877 · Cited by 23

Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

Geiß, C., Priesmeier, P., Pelizari, P. A., Calderon, A. R. S., Schöepfer, E., Riedlinger, T., Vilar-Vega, M., María, H. S., Zapata, J. C. G., Pittore, M., et al. (2022) · Natural Hazards · doi:10.1007/s11069-022-05672-6 · Cited by 23

Potential of Using Night-Time Light to Proxy Social Indicators for Sustainable Development

Andries, A., Morse, S., Murphy, R., Sadhukhan, J., Martínez-Hernández, E., Amezcua‐Allieri, M. A., & Aburto, J. (2023) · Remote Sensing · doi:10.3390/rs15051209 · Cited by 24

Variation in youth and young adult homicide rates and their association with city characteristics in Latin America: the SALURBAL study

Friche, A. A. D. L., Silva, U. M., Bilal, U., Sarmiento, O. L., Dias, M. A. D. S., Prado-Galbarro, F. J., Briceño-León, R., Alazraqui, M., Roux, A. V. D., & Caiaffa, W. T. (2023) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2023.100476 · Cited by 23

Cross-city Landuse classification of remote sensing images via deep transfer learning

Zhao, X., Hu, J., Mou, L., Xiong, Z., & Zhu, X. X. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103358 · Cited by 25

A Novel Sample Selection Method for Impervious Surface Area Mapping Using JL1-3B Nighttime Light and Sentinel-2 Imagery

Tang, P., Du, P., Lin, C., Guo, S., & Qie, L. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3004654 · Cited by 23

GIS-Based Modeling of Human Settlement Suitability for the Belt and Road Regions

Li, W., Li, P., Feng, Z., & Xiao, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19106044 · Cited by 22

Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA)

Nederhoff, K., Crosby, S. C., Arendonk, N. R. V., Grossman, E. E., Tehranirad, B., Leijnse, T., Klessens, W., & Barnard, P. L. (2024) · Water · doi:10.3390/w16020346 · Cited by 22

Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda

Kang, S. Y., Battle, K. E., Gibson, H. S., Cooper, L. V., Maxwell, K., Kamya, M., Lindsay, S. W., Dorsey, G., Greenhouse, B., Rodriguez-Barraquer, I., et al. (2018) · Gates Open Research · doi:10.12688/gatesopenres.12838.2 · Cited by 25

More Urban Constructions for Whom? Drivers of Urban Built-Up Expansion Across the World from 1990 to 2015

Denis, É. (2020) · Lecture notes in morphogenesis · doi:10.1007/978-3-030-36656-8_13 · Cited by 21

A Review of Human Settlement Research on Climate Change Response under Carbon-Oriented: Literature Characteristics, Progress and Trends

An, N., Yao, Q., & Shen, Q. (2022) · Buildings · doi:10.3390/buildings12101614 · Cited by 22

Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach

Han, L., Lu, L., Lu, J., Liu, X., Zhang, S., Luo, K., He, D., Wang, P., Guo, H., & Li, Q. (2022) · Remote Sensing · doi:10.3390/rs14194985 · Cited by 21

Assessing nighttime lights for mapping the urban areas of 50 cities across the globe

Bagan, H., Borjigin, H., & Yamagata, Y. (2018) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808317752926 · Cited by 21

Mapping Impervious Surfaces Precisely—a GIS-Based Methodology Combining Vector Data and High-Resolution Airborne Imagery

Schmidt, S., & Barron, C. (2020) · Journal of Geovisualization and Spatial Analysis · doi:10.1007/s41651-020-00055-6 · Cited by 21

A crowdsourced global data set for validating built-up surface layers

See, L., Georgieva, I., Duerauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sîrbu, F., Ahmed, R., et al. (2022) · Scientific Data · doi:10.1038/s41597-021-01105-4 · Cited by 21

Improving 30-meter global impervious surface area (GISA) mapping: New method and dataset

Ren, H., Huang, X., Yang, J., & Zhou, G. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.12.023 · Cited by 20

The utility of built environment geospatial data for high-resolution dasymetric global population modeling

Rubinyi, S. L., Blankespoor, B. A., & Hall, J. W. (2021) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101594 · Cited by 19

Mapping Rural Settlements from Landsat and Sentinel Time Series by Integrating Pixel- and Object-Based Methods

Xu, R. (2021) · Land · doi:10.3390/land10030244 · Cited by 19

Urban bat pups take after their mothers and are bolder and faster learners than rural pups

Harten, L. M., Gonceer, N., Handel, M., Dash, O., Fokidis, H. B., & Yovel, Y. (2021) · BMC Biology · doi:10.1186/s12915-021-01131-z · Cited by 19

Multi-polarization C-band SAR imagery to quantify damage levels due to the Central Italy earthquake

Ferrentino, E., Nunziata, F., Bignami, C., Graziani, L., Maramai, A., & Migliaccio, M. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1933247 · Cited by 19

Landscape change and its drivers: a Southern African perspective

Musakwa, W., & Wang, S. (2018) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2018.05.001 · Cited by 19

Using Information on Settlement Patterns to Improve the Spatial Distribution of Population in Coastal Impact Assessments

Merkens, J.-L., & Vafeidis, A. T. (2018) · Sustainability · doi:10.3390/su10093170 · Cited by 18

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

Yang, X., Yao, C., Chen, Q., Ye, T., & Jin, C. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16204012 · Cited by 18

Extracting physical urban areas of 81 major Chinese cities from high-resolution land uses

Zhang, X., Du, S., Zhou, Y., & Xu, Y. F. (2022) · Cities · doi:10.1016/j.cities.2022.104061 · Cited by 18

Fruit bats in flight: a look into the movements of the ecologically important Eidolon helvum in Tanzania

Randhawa, N., Bird, B. H., VanWormer, E., Sijali, Z., Kilonzo, C., Msigwa, A., Ekiri, A. B., Samson, A. J., Epstein, J. H., Wolking, D. J., et al. (2020) · One Health Outlook · doi:10.1186/s42522-020-00020-9 · Cited by 18

Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping

Borlaf-Mena, I., Badea, O. N., & Tanase, M. A. (2021) · Remote Sensing · doi:10.3390/rs13234814 · Cited by 18

Multi-temporal LCZ maps for Canadian functional urban areasPreprint

Demuzere, M., Mihara, T., Redivo, C., Feddema, J. J., & Setton, E. M. (2020) · doi:10.31219/osf.io/h5tm6 · Cited by 18

How Good Are Global Layers for Mapping Rural Settlements? Evidence from China

Wang, N., Zhang, X., Yao, S., Wu, J., & Xia, H. (2022) · Land · doi:10.3390/land11081308 · Cited by 17

Automatic built-up area extraction by feature-level fusion of Luojia 1–01 nighttime light and Sentinel satellite imageries in Google Earth Engine

Samadzadegan, F., Toosi, A., & Javan, F. D. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.05.015 · Cited by 17

Characterizing urban densification in the city of Wuhan using time-series building information

Cao, Q., Huang, H., Wang, W., & Wang, L. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01718-7 · Cited by 17

Generating high-accuracy urban distribution map for short-term change monitoring based on convolutional neural network by utilizing SAR imagery

Iino, S., Ito, R., Doi, K., Imaizumi, T., & Hikosaka, S. (2017) · doi:10.1117/12.2277901 · Cited by 17

Spatiotemporal Analysis of Venezuela's Nighttime Light During the Socioeconomic Crisis

Zhang, L., Li, X., & Chen, F. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2995695 · Cited by 17

Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data

Hu, Y., Chen, J., Cao, X., Chen, X., Cui, X., & Gan, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3060170 · Cited by 17

Mapping Blue and Red Color-Coated Steel Sheet Roof Buildings over China Using Sentinel-2A/B MSIL2A Images

Samat, A., Gamba, P. E., Wang, W., Luo, J., Li, E., Liu, S., Du, P., & Abuduwaili, J. (2022) · Remote Sensing · doi:10.3390/rs14010230 · Cited by 17

Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

Palacios‐Lopez, D., Esch, T., MacManus, K., Marconcini, M., Sorichetta, A., Yetman, G., Zeidler, J., Dech, S. W., Tatem, A. J., & Reinartz, P. H. (2022) · Remote Sensing · doi:10.3390/rs14020325 · Cited by 16

Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C/L Band Data

Deng, J. G., Dai, K., Liang, R., Chen, L., Wen, N., Zheng, G., & Xu, H. (2023) · Remote Sensing · doi:10.3390/rs15184538 · Cited by 16

Climate hazards in Latin American cities: Understanding the role of the social and built environments and barriers to adaptation action

Slovic, A. D., Indvik, K., Martins, L. S., Kephart, J. L., Swanson, S., Quistberg, D. A., Moran, M. R., Bakhtsiyarava, M., Zavaleta-Cortijo, C., Gouveia, N., et al. (2024) · Climate Risk Management · doi:10.1016/j.crm.2024.100625 · Cited by 24

Ten Years of Experience with Scientific TerraSAR-X Data Utilization

Roth, A., Marschalk, U., Winkler, K., Schättler, B., Huber, M., Georg, I., Künzer, C., & Dech, S. W. (2018) · Remote Sensing · doi:10.3390/rs10081170 · Cited by 16

Where We Live—A Summary of the Achievements and Planned Evolution of the Global Urban Footprint

Esch, T., Bachofer, F., Heldens, W., Hirner, A., Marconcini, M., Palacios‐Lopez, D., Roth, A., Üreyen, S., Zeidler, J., Dech, S. W., et al. (2018) · Remote Sensing · doi:10.3390/rs10060895 · Cited by 97

Global Urban Footprint -Eine neue Dimension der globalen Siedlungskartierung aus dem Weltall

Zeidler, J., Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M., & Üreyen, S. (2017) · elib (German Aerospace Center)

The global urban footprint — Processing status and cross comparison to existing human settlement products

Felbier, A., Esch, T., Heldens, W., Marconcini, M., Zeidler, J., Roth, A., Klotz, M., Wurm, M., & Taubenböck, H. (2014) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2014.6947572 · Cited by 11

Unsupervised high-resolution global monitoring of urban settlements

Marconcini, M., Esch, T., Felbier, A., & Heldens, W. (2013) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2013.6723770 · Cited by 7

Extraction and Validation of Database of Urban and Non-Urban Points from Remote Sensing Data

(2025) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0712.1008 · Cited by 2