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Extracting Urban Deprivation Indicators Using Superspectral Very-High-Resolution Satellite Imagery.

, , , and . IGARSS, page 2114-2117. IEEE, (2021)

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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)UAVs for Fine-Scale Open-Source Landfill Mapping., , , , and . IGARSS, page 8217-8220. IEEE, (2021)Gridded Urban Deprivation Probability from Open Optical Imagery and Dual-Pol Sar Data., , , , , and . IGARSS, page 2110-2113. IEEE, (2021)Mapping slums and model population density using earth observation data and open source solutions., , , , , and . JURSE, page 1-4. IEEE, (2019)Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA., , , and . ISPRS Int. J. Geo Inf., 11 (11): 550 (2022)Extracting Urban Deprivation Indicators Using Superspectral Very-High-Resolution Satellite Imagery., , , and . IGARSS, page 2114-2117. IEEE, (2021)EO-Based Low-Cost Frameworks to Address Global Urban Data GAPS on Deprivation and Multiple Hazards., , , , , , and . IGARSS, page 2106-2109. IEEE, (2021)A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests., and . ISPRS Int. J. Geo Inf., 11 (9): 471 (2022)Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery., , , , , and . Remote. Sens., 11 (5): 597 (2019)Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda., , , and . JURSE, page 1-4. IEEE, (2023)