Abstract
Machine learning techniques have been frequently applied to map urban deprivation (commonly referred to as slums) in very high-resolution satellite images. Among these, Deep Convolutional Neural Networks have shown exceptional efficiency in automated deprivation mapping at the local scale. Yet these networks have never been used to map very small
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