  Spatially consistent and up-to-date maps of human settlements are crucial for
addressing policies related to urbanization and sustainability, especially in
the era of an increasingly urbanized world.The availability of open and free
Sentinel-2 data of the Copernicus Earth Observation program offers a new
opportunity for wall-to-wall mapping of human settlements at a global
scale.This paper presents a deep-learning-based framework for a fully automated
extraction of built-up areas at a spatial resolution of 10 m from a global
composite of Sentinel-2 imagery.A multi-neuro modeling methodology building on
a simple Convolution Neural Networks architecture for pixel-wise image
classification of built-up areas is developed.The core features of the proposed
model are the image patch of size 5 x 5 pixels adequate for describing built-up
areas from Sentinel-2 imagery and the lightweight topology with a total number
of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened
layers.The deployment of the model on the global Sentinel-2 image composite
provides the most detailed and complete map reporting about built-up areas for
reference year 2018. The validation of the results with an independent
reference data-set of building footprints covering 277 sites across the world
establishes the reliability of the built-up layer produced by the proposed
framework and the model robustness.
