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Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas.

, , , , , , and . Comput. Environ. Urban Syst., (2022)

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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)"Domains of deprivation framework" for mapping slums, informal settlements, and other deprived areas in LMICs to improve urban planning and policy: A scoping review., , , , , , , and . Comput. Environ. Urban Syst., (2022)On the knowledge gain of urban morphology from space., , , , and . Comput. Environ. Urban Syst., (2022)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)In-Between the Lines and Pixels: Cartography's Transition from Tool of the State to Humanitarian Mapping of Deprived Urban Areas., , and . Digit. Soc., (July 2022)Extraction of Slum Areas From VHR Imagery Using GLCM Variance., , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 9 (5): 1830-1840 (2016)Understanding heterogeneity in metropolitan India: The added value of remote sensing data for analyzing sub-standard residential areas., , , , and . Int. J. Appl. Earth Obs. Geoinformation, 12 (5): 359-374 (2010)The Temporal Dynamics of Slums Employing a CNN-Based Change Detection Approach., , and . Remote Sensing, 11 (23): 2844 (2019)A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2022)