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Spatially consistent and up-to-date maps of human settlements are crucial foraddressing policies related to urbanization and sustainability, especially inthe era of an increasingly urbanized world.The availability of open and freeSentinel-2 data of the Copernicus Earth Observation program offers a newopportunity for wall-to-wall mapping of human settlements at a globalscale.This paper presents a deep-learning-based framework for a fully automatedextraction of built-up areas at a spatial resolution of 10 m from a globalcomposite of Sentinel-2 imagery.A multi-neuro modeling methodology building ona simple Convolution Neural Networks architecture for pixel-wise imageclassification of built-up areas is developed.The core features of the proposedmodel are the image patch of size 5 x 5 pixels adequate for describing built-upareas from Sentinel-2 imagery and the lightweight topology with a total numberof 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattenedlayers.The deployment of the model on the global Sentinel-2 image compositeprovides the most detailed and complete map reporting about built-up areas forreference year 2018. The validation of the results with an independentreference data-set of building footprints covering 277 sites across the worldestablishes the reliability of the built-up layer produced by the proposedframework and the model robustness.

Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery