Binzel, C. and Fehr, D.: Social distance and trust: Experimental evidence from a slum in Cairo, J. Dev. Econ., 103, 99–106, https://doi.org/10.1016/j.jdeveco.2013.01.009, 2013.
Björkman, L.: Becoming a slum: from municipal colony to illegal settlement in liberalization era Mumbai. Contesting the Indian city: global visions and the politics of the local, Wiley, 208–240, https://doi.org/10.1002/9781118295823.ch8, 2013.
Breuer, J. H. and Friesen, J.: Methods to assess spatio-temporal changes of slum populations, Cities 143, 104582, https://doi.org/10.1016/j.cities.2023.104582, 2023.
Breuer, J. H., Friesen, J., Taubenböck, H., Wurm, M., and Pelz, P. F.: The unseen population: Do we underestimate slum dwellers in cities of the Global South?, Habitat Int., 148, 103056, https://doi.org/10.1016/j.habitatint.2024.103056, 2024.
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Herold, M., and Fritz, S.: Copernicus global land service: land cover 100 m: collection 3: epoch 2019: Globe, Zenodo [data set], https://doi.org/10.5281/zenodo.3939050, 2020.
Burgert, C.R., Colston, J., Roy, T., and Zachary, B.: Geographic displacement procedure and georeferenced data release policy for the Demographic and Health Surveys, Icf International, https://doi.org/10.13140/RG.2.1.4887.6563, 2013.
Burke, M., Driscoll, A., Lobell, D. B., and Ermon, S.: Using satellite imagery to understand and promote sustainable development, Science, 371, eabe8628, https://doi.org/10.1126/science.abe8628, 2021.
Butera, F. M., Caputo, P., Adhikari, R. S., and Mele, R.: Energy access in informal settlements. Results of a wide on site survey in Rio De Janeiro, Energy Policy, 134, 110943, https://doi.org/10.1016/j.enpol.2019.110943, 2019.
Byuro, B. P. K. N.: Census of slum areas and floating population 2014,
http://arks.princeton.edu/ark:/88435/dsp01wm117r42q (last access: 1 August 2024), 2015.
Chandramouli, C.: Housing stock, amenities and assets in slums – Census 2011, Office of the Registrar General and Census Commissioner, New Delhi,
https://censusindia.gov.in/ (last access: 14 May 2026), 2011.
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, San Francisco, California, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., Wu, J., Liao, L., and Shi, K.: The global NPP-VIIRS-like nighttime light data (Version 2) for 1992–2025, Harvard Dataverse, V10, https://doi.org/10.7910/DVN/YGIVCD, 2020.
Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., and Wu, J.: An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration, Earth Syst. Sci. Data, 13, 889–906, https://doi.org/10.5194/essd-13-889-2021, 2021.
da Fonseca Feitosa, F., Vieira Vasconcelos, V., Moutinho Duque de Pinho, C., Frizzi Galdino da Silva, G., da Silva Gonçalves, G., Correa Danna, L. C., and Seixas Lisboa, F.: IMMerSe: An integrated methodology for mapping and classifying precarious settlements, Appl. Geogr., 133, 102494, https://doi.org/10.1016/j.apgeog.2021.102494, 2021.
Davis, M.: Planet of slums, New Perspect. Quart., 30, 11–12, https://doi.org/10.1111/npqu.11395, 2013.
Doe, B., Peprah, C., and Chidziwisano, J. R.: Sustainability of slum upgrading interventions: Perception of low-income households in Malawi and Ghana, Cities 107, 102946, https://doi.org/10.1016/j.cities.2020.102946, 2020.
do Nascimento, G. A., Giannotti, M., Regueira, T. A., and Tomasiello, D. B.: Identifying slum areas: A multidimensional analysis leveraging with explanatory machine learning techniques, Sustain. Cities Soc., 131, 106645, https://doi.org/10.1016/j.scs.2025.106645, 2025.
Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T.: VIIRS night-time lights, Int. J. Remote Sens., 38, 5860–5879, https://doi.org/10.1080/01431161.2017.1342050, 2017.
Engin, Z., van Dijk, J., Lan, T., Longley, P. A., Treleaven, P., Batty, M., and Penn, A.: Data-driven urban management: Mapping the landscape, J. Urban Manage., 9, 140–150, https://doi.org/10.1016/j.jum.2019.12.001, 2020.
Ezeh, A., Oyebode, O., Satterthwaite, D., Chen, Y.-F., Ndugwa, R., Sartori, J., Mberu, B., Melendez-Torres, G. J., Haregu, T., and Watson, S. I.: The history, geography, and sociology of slums and the health problems of people who live in slums, Lancet, 389, 547–558, https://doi.org/10.1016/s0140-6736(16)31650-6, 2017.
Fu, B., Wang, S., Zhang, J., Hou, Z., and Li, J.: Unravelling the complexity in achieving the 17 sustainable-development goals, Natl. Sci. Rev., 6, 386–388, https://doi.org/10.1093/nsr/nwz038, 2019.
Gram-Hansen, B. J., Helber, P., Varatharajan, I., Azam, F., Coca-Castro, A., Kopackova, V., and Bilinski, P.: Mapping informal settlements in developing countries using machine learning and low resolution multi-spectral data, in: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 361–368, https://doi.org/10.1145/3306618.3314253, 2019.
Guan, X., Wei, H., Lu, S., Dai, Q., and Su, H.: Assessment on the urbanization strategy in China: Achievements, challenges and reflections, Habitat Int., 71, 97–109, https://doi.org/10.1016/j.habitatint.2017.11.009, 2018.
Guilmoto, C. Z. and Rajan, S. I.: Fertility at the district level in India: Lessons from the 2011 census, Economic and Political weekly, 59–70,
http://www.ceped.org/wp (last access: 1 August 2024), 2013.
Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G., Fishman, T., and Lanau, M.: High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany, Environ. Sci. Technol., 55, 3368–3379, https://doi.org/10.1021/acs.est.0c05642, 2021.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778, https://doi.org/10.1109/cvpr.2016.90, 2016.
Hohmann, J.: The right to housing: Law, concepts, possibilities, Bloomsbury Publishing, https://doi.org/10.5040/9781472566416, 2013.
Hsu, F.-C., Baugh, K. E., Ghosh, T., Zhizhin, M., and Elvidge, C. D.: DMSP-OLS radiance calibrated nighttime lights time series with intercalibration, Remote Sens., 7, 1855–1876, https://doi.org/10.3390/rs70201855, 2015.
Ibrahim, M. R., Titheridge, H., Cheng, T., and Haworth, J.: predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning, Comput. Environ. Urban Syst., 76, 31–56, https://doi.org/10.1016/j.compenvurbsys.2019.03.005, 2019.
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S.: Combining satellite imagery and machine learning to predict poverty, Science, 353, 790–794, https://doi.org/10.1126/science.aaf7894, 2016.
Kingma, D. P. and Ba, J. A.: A method for stochastic optimization, arXiv [preprint], arXiv:1412.6980, https://doi.org/10.48550/arXiv.1412.6980, 2014.
Li, D., Sun, L., Yu, Y., and Tian, P.: Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data, Zenodo [data set], https://doi.org/10.5281/zenodo.13779002, 2025.
Mahabir, R., Agouris, P., Stefanidis, A., Croitoru, A., and Crooks, A. T.: Detecting and mapping slums using open data: A case study in Kenya, Int. J. Digit. Earth, 13, 683–707, https://doi.org/10.1080/17538947.2018.1554010, 2020.
Marx, B., Stoker, T., and Suri, T.: The economics of slums in the developing world, J. Econ. Perspect., 27, 187–210, https://doi.org/10.1257/jep.27.4.187, 2013.
Medeiros, S. D. S., Pinto, T. F., Hernan Salcedo, I., Cavalcante, A. D. M. B., Perez Marin, A. M., and Tinôco, L. B. D. M.: Sinopse do censo demográfico para o semiárido brasileiro, INSA – Instituto Nacional de Seminário,
http://livroaberto.ibict.br/handle/1/941 (last access: 1 August 2024), 2012.
Meena, S., Nava, L., Bhuyan, K., Puliero, S., Soares, L., Dias, H., Floris, M., and Catani, F.: HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery, Earth Syst. Sci. Data, 15, 3283–3298, https://doi.org/10.5194/essd-15-3283-2023, 2023.
Moreno, E. L.: Slums of the world: The face of urban poverty in the new millennium?: Monitoring the millennium development goal, target 11 – world-wide slum dweller estimation, Habitat,
https://digitallibrary.un.org/record/515731 (last access: 25 October 2024), 2003.
Mwaniki, D. and Ndugwa, R.: The Global Urban Monitoring Approach Taken by UN-Habitat, Stadtentwicklung beobachten, messen und umsetzen, IzR – Informationen zur Raumentwicklung, 32–43,
https://biblioscout.net/article/99.140005/izr202101003201 (last access: 25 October 2024), 2021.
Nielsen, D. Tree boosting with XGBoost: Why does XGBoost win “every” machine learning competition?, MS thesis, Norwegian University of Science and Technology, Trondheim, Norway,
http://hdl.handle.net/11250/2433761 (last access: 25 October 2024), 2016.
Nowak, M.: Introduction to the international human rights regime, Brill, https://doi.org/10.1163/9789004479074, 2003.
Owusu, M., Kuffer, M., Belgiu, M., Grippa, T., Lennert, M., Georganos, S., and Vanhuysse, S.: Towards user-driven earth observation-based slum mapping, Comput. Environ. Urban Syst., 89, 101681, https://doi.org/10.1016/j.compenvurbsys.2021.101681, 2021.
Pan, S. J. and Yang, Q.: A survey on transfer learning, IEEE T. Knowl. Data Eng., 22, 1345–1359, https://doi.org/10.1109/TKDE.2009.191, 2009.
Parikh, P., Parikh, H., and McRobie, A.: The role of infrastructure in improving human settlements, Proc. Inst. Civ. Eng.-Urban Design Plan., 166, 101–118, https://doi.org/10.1680/udap.10.00038, 2013.
Patel, A., Joseph, G., Shrestha, A., and Foint, Y.: Measuring deprivations in the slums of Bangladesh: implications for achieving sustainable development goals, Hous. Soc., 46, 81–109, 2019.
Patel, N. N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F. R., Tatem, A. J., and Trianni, G.: Multitemporal settlement and population mapping from Landsat using Google Earth Engine, Int. J. Appl. Earth Obs. Geoinf., 35, 199–208, https://doi.org/10.1016/j.jag.2014.09.005, 2015.
Persello, C. and Kuffer, M.: Towards uncovering socio-economic inequalities using VHR satellite images and deep learning, in: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 3747–3750, https://doi.org/10.1109/igarss39084.2020.9324399, 2020.
Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A.J., Freire, S., Halkia, M., Julea, A., Kemper, T., Soille, P., and Syrris, V.: Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014, Publications Office of the European Union, Luxembourg, https://doi.org/10.2788/253582, 2016.
Ramraj, S., Uzir, N., Sunil, R., and Banerjee, S.: Experimenting XGBoost algorithm for prediction and classification of different datasets, Int. J. Control Theory Appl., 9, 651–662, 2016.
Rentschler, J., Avner, P., Marconcini, M., Su, R., Strano, E., Vousdoukas, M., and Hallegatte, S.: Global evidence of rapi
d urban growth in flood zones since 1985, Nature, 622, 87–92, https://doi.org/10.1038/s41586-023-06468-9, 2023.
Robinson, C., Hohman, F., and Dilkina, B.: A deep learning approach for population estimation from satellite imagery, in: Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, 47–54, https://doi.org/10.1145/3149858.3149863, 2017.
Rogler, L. H.: Slum Neighborhoods in Latin America, J. Inter-Am. Stud., 9, 507–528, https://doi.org/10.2307/164857, 1967.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., and Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., and Bernstein, M.: Imagenet large scale visual recognition challenge, Int. J. Comput. Vision, 115, 211–252, https://doi.org/10.1007/s11263-015-0816-y, 2015.
Rutstein, S. O. and Staveteig, S.: Making the demographic and health surveys wealth index comparable, ICF international Rockville, MD,
https://www.dhsprogram.com/pubs/pdf/MR9/MR9.pdf (last access: 1 August 2023), 2014.
Sabry, S.: Poverty lines in Greater Cairo: underestimating and misrepresenting poverty, IIED,
https://www.iied.org/10572iied (last access: 1 August 2024), 2009.
Satterthwaite, D., Archer, D., Colenbrander, S., Dodman, D., Hardoy, J., Mitlin, D., and Patel, S.: Building resilience to climate change in informal settlements, One Earth, 2, 143–156, https://doi.org/10.1016/j.oneear.2020.02.002, 2020.
Schetke, S., Haase, D., and Kötter, T.: Towards sustainable settlement growth: A new multi-criteria assessment for implementing environmental targets into strategic urban planning, Environ. Impact Assess. Rev., 32, 195–210, https://doi.org/10.1016/j.eiar.2011.08.008, 2012.
Schiavina, M., Melchiorri, M., Pesaresi, M., Politis, P., Carneiro Freire, S., Maffenini, L., Florio, P., Ehrlich, D., Goch, K., and Tommasi, P.: GHSL data package 2022: Public release GHS P2022, KJ-07–22–357-EN-N (online), KJ-07–22–357-EN-C (print), European Union, https://doi.org/10.2760/19817, 2022.
Shi, Q., Liu, M., Marinoni, A., and Liu, X.: UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework, Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, 2023.
Sietchiping, R. and Yoon, H. J.: What drives slum persistence and growth? Empirical evidence from sub-Saharan Africa, Int. J. Adv. Stud. Res. Africa, 1, 1–22, 2010.
Singh, B. N: Socio-economic conditions of slums dwellers: a theoretical study, Kaav Int. J. Arts Human. Social Sci., 3, 5–20, 2016.
Smith, A., Bates, P. D., Wing, O., Sampson, C., Quinn, N., and Neal, J.: New estimates of flood exposure in developing countries using high-resolution population data, Nat. Commun., 10, 1814, https://doi.org/10.1038/s41467-019-09282-y, 2019.
Stevens, F. R., Gaughan, A. E., Linard, C., and Tatem, A. J.: Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data, PloS One, 10, e0107042, https://doi.org/10.1371/journal.pone.0107042, 2015.
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., and Brisco, B.: Google Earth Engine for geo-big data applications: A meta-analysis and systematic review, ISPRS J. Photogram. Remote Sens., 164, 152–170, https://doi.org/10.1016/j.isprsjprs.2020.04.001, 2020.
Taubenböck, H. and Wurm, M.: Globale Urbanisierung–Markenzeichen des 21. Jahrhunderts, Globale Urbanisierung: Perspektive aus dem All, Springer, 5–10, https://doi.org/10.1007/978-3-662-44841-0_2, 2015.
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging reveals increased proportion of population exposed to floods, Nature, 596, 80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Thomson, D. R., Linard, C., Vanhuysse, S., Steele, J. E., Shimoni, M., Siri, J., Caiaffa, W. T., Rosenberg, M., Wolff, E., and Grippa, T.: Extending data for urban health decision-making: a menu of new and potential neighborhood-level health determinants datasets in LMICs, J. Urban Health, 96, 514–536, https://doi.org/10.1007/s11524-019-00363-3, 2019.
Thomson, D. R., Kuffer, M., Boo, G., Hati, B., Grippa, T., Elsey, H., Linard, C., Mahabir, R., Kyobutungi, C., and Maviti, J.: Need for an integrated deprived area “slum” mapping system (IDEAMAPS) in low-and middle-income countries (LMICs), Social Sci., 9, 80, https://doi.org/10.3390/socsci9050080, 2020.
Thomson, D. R., Stevens, F. R., Chen, R., Yetman, G., Sorichetta, A., and Gaughan, A. E.: Improving the accuracy of gridded population estimates in cities and slums to monitor SDG 11: Evidence from a simulation study in Namibia, Land Use Policy, 123, 106392, https://doi.org/10.1016/j.landusepol.2022.106392, 2022.
Tian, P., Zhong, H., Chen, X., Feng, K., Sun, L., Zhang, N., Shao, X., Liu, Y., and Hubacek, K.: Keeping the global consumption within the planetary boundaries, Nature, 635, 625–630, https://doi.org/10.1038/s41586-024-08154-w, 2024.
Tjia, D. and Coetzee, S.: Geospatial information needs for informal settlement upgrading – A review, Habitat Int., 122, 102531, https://doi.org/10.1016/j.habitatint.2022.102531, 2022.
Trindade, T. C., MacLean, H. L., and Posen, I. D.: Slum infrastructure: Quantitative measures and scenarios for universal access to basic services in 2030, Cities, 110, 103050, https://doi.org/10.1016/j.cities.2020.103050, 2021.
UN‐Habitat: The challenge of slums: global report on human settlements 2003, Manage. Environ. Qual., 15, 337–338, https://doi.org/10.1108/meq.2004.15.3.337.3, 2004.
UN-Habitat: World Cities Report 2020: The Value of Sustainable Urbanization,
https://unhabitat.org/world-cities-report-2020-the-value-of-sustainable-urbanization (last access: 1 May 2025), 2020.
UN-Habitat: Urban indicators database.
https://data.unhabitat.org/pages/housing-slums-and-informal-settlements (last access: 1 May 2025), 2021.
Weber, H.: Politics of `leaving no one behind': contesting the 2030 Sustainable Development Goals agenda, The Politics o
f Destination in the 2030 Sustainable Development Goals, Routledge, 64–79, https://doi.org/10.4324/9780429490507-4, 2018.
World Bank: World Bank country and lending groups,
https://datahelpdesk.worldbank.org/knowledgebase/articles/906519 (last access: 1 August 2024), 2022.
World Bank Group: Population living in slums (% of urban population),
https://data.worldbank.org/indicator/EN.POP.SLUM.UR.ZS (last access: 1 August 2024), 2018.
Wu, Z., Shen, C., and Van Den Hengel, A.: Wider or deeper: Revisiting the resnet model for visual recognition, Pattern Recog., 90, 119–133, https://doi.org/10.1016/j.patcog.2019.01.006, 2019.
Wurm, M., Taubenböck, H., Weigand, M., and Schmitt, A.: Slum mapping in polarimetric SAR data using spatial features, Remote Sens. Environ., 194, 190–204, https://doi.org/10.1016/j.rse.2017.03.030, 2017.
Wurm, M., Stark, T., Zhu, X. X., Weigand, M., and Taubenböck, H.: Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks, ISPRS J. Photogram. Remote Sens., 150, 59–69, https://doi.org/10.1016/j.isprsjprs.2019.02.006, 2019.
Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., and Burke, M.: Using publicly available satellite imagery and deep learning to understand economic well-being in Africa, Nat. Commun., 11, 2583, https://doi.org/10.1038/s41467-020-16185-w, 2020.
Zhou, Y., Li, X., Chen, W., Meng, L., Wu, Q., Gong, P., and Seto, K. C.: Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South, P. Natla. Acad. Sci. USA, 119, e2214813119, https://doi.org/10.1073/pnas.2214813119, 2022.
