A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks
This work addresses the need for large-scale urban mapping to monitor global urbanization, particularly in cases lacking up-to-date ground truth, though it is incremental as it builds on existing CNN-based approaches.
The paper tackles the problem of mapping human settlement extent from Sentinel-2 satellite images by developing a deep-learning framework using regionally available geo-products for training, achieving consistent results across 10 global test areas and demonstrating scalability to regional and country-wide scales.
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.