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.

人类住区范围 (HSE) 信息是全球城市化以及由此产生的人类对自然环境压力的重要指标。因此，绘制 HSE 地图对于解决地方、区域甚至全球范围内的各种环境问题至关重要。本文提出了一个基于深度学习的框架，使用区域可用的地理产品作为训练标签，从多光谱 Sentinel-2 数据自动映射 HSE。一个直接、简单但有效的基于全卷积网络的架构 Sen2HSE 被实现作为框架内语义分割的示例。该框架针对均匀分布在测试区域上的手动标记检查点和 OpenStreetMap 构建层进行了验证。 HSE 映射结果与多个基准产品进行了广泛比较，以便彻底评估所提出的 HSE 映射框架的有效性。 HSE 测绘能力在全球 10 多个代表性地区得到了一致证明。我们还从我们的框架中展示了一个区域范围和一个全国范围的 HSE 绘图示例，以展示升级的潜力。这项研究的结果有助于将基于 CNN 的大规模城市测绘方法的适用性推广到没有最新且准确的地面实况的情况，以及随后对全球城市化的监测。