  Human settlement extent (HSE) information is a valuable indicator of
world-wide urbanization as well as the resulting human pressure on the natural
environment. Therefore, mapping HSE is critical for various environmental
issues at local, regional, and even global scales. This paper presents a
deep-learning-based framework to automatically map HSE from multi-spectral
Sentinel-2 data using regionally available geo-products as training labels. A
straightforward, simple, yet effective fully convolutional network-based
architecture, Sen2HSE, is implemented as an example for semantic segmentation
within the framework. The framework is validated against both manually labelled
checking points distributed evenly over the test areas, and the OpenStreetMap
building layer. The HSE mapping results were extensively compared to several
baseline products in order to thoroughly evaluate the effectiveness of the
proposed HSE mapping framework. The HSE mapping power is consistently
demonstrated over 10 representative areas across the world. We also present one
regional-scale and one country-wide HSE mapping example from our framework to
show the potential for upscaling. The results of this study contribute to the
generalization of the applicability of CNN-based approaches for large-scale
urban mapping to cases where no up-to-date and accurate ground truth is
available, as well as the subsequent monitor of global urbanization.
