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The utility of the co-occurrence matrix to extract slum areas from VHR imagery., , , and . JURSE, page 1-4. IEEE, (2015)EO-Based Low-Cost Frameworks to Address Global Urban Data GAPS on Deprivation and Multiple Hazards., , , , , , and . IGARSS, page 2106-2109. IEEE, (2021)Extracting Urban Deprivation Indicators Using Superspectral Very-High-Resolution Satellite Imagery., , , and . IGARSS, page 2114-2117. IEEE, (2021)Towards Uncovering Socio-Economic Inequalities Using VHR Satellite Images and Deep Learning., and . IGARSS, page 3747-3750. IEEE, (2020)Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas., , , , , , and . Comput. Environ. Urban Syst., (2022)Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information., , , and . Remote. Sens., 11 (10): 1174 (2019)Three-Dimensional Modelling of Past and Present Shahjahanabad through Multi-Temporal Remotely Sensed Data., , , , and . Remote. Sens., 15 (11): 2924 (2023)An exploration of natural capital in the context of multiple deprivations., , , and . JURSE, page 157-160. IEEE, (2011)The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery., , and . Comput. Environ. Urban Syst., (2014)The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets., , , , and . ISPRS Int. J. Geo Inf., 11 (7): 403 (2022)