Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the complex interactions between human activities and natural resources. Existing global urban-rural datasets such as such as GHSL-SMOD, GHS Degree of Urbanisation, and GRUMP are often spatially coarse, methodologically inconsistent, and poorly adapted to heterogeneous regions such as Africa, which limits their usefulness for policy and research. Their coarse grids and rule-based classification methods obscure small or informal settlements, and produce inconsistencies between countries. In this study, we develop a DeepLabV3-based deep learning framework that integrates multi-source data, including Landsat-8 imagery, VIIRS nighttime lights, ESRI Land Use Land Cover (LULC), and GHS-SMOD, to produce a 10m resolution urban-rural map across the African continent from 2016 to 2022. The use of Landsat data also highlights the potential to extend this mapping approach historically, reaching back to the 1990s. The model employs semantic segmentation to capture fine-scale settlement morphology, and its outputs are validated using the Demographic and Health Surveys (DHS) dataset, which provides independent, survey-based urban-rural labels. The model achieves an overall accuracy of 65% and a Kappa coefficient of 0.47 at the continental scale, outperforming existing global products such as SMOD. The resulting High-Resolution Urban-Rural (HUR) dataset provides an open and reproducible framework for mapping human settlements, enabling more context-aware analyses of Africa's rapidly evolving settlement systems. We release a continent-wide urban-rural dataset covering the period from 2016 to 2022, offering a new source for high-resolution settlement mapping in Africa.

翻译：城乡区域的精确且一致的制图对于可持续发展、空间规划和政策设计至关重要。这对于模拟人类活动与自然资源之间复杂的相互作用尤为重要。现有的全球城乡数据集，如GHSL-SMOD、GHS城市化程度和GRUMP，通常空间分辨率粗糙、方法学不一致，并且难以适应非洲等异质性区域，这限制了其在政策与研究中的实用性。其粗糙的网格和基于规则的分类方法掩盖了小型或非正式聚落，并导致国家间的不一致性。在本研究中，我们开发了一个基于DeepLabV3的深度学习框架，该框架集成了多源数据，包括Landsat-8影像、VIIRS夜间灯光、ESRI土地利用土地覆盖（LULC）和GHS-SMOD，以生成2016年至2022年整个非洲大陆的10米分辨率城乡地图。Landsat数据的使用也凸显了将该制图方法在历史上进行延伸的潜力，可追溯至20世纪90年代。该模型采用语义分割来捕捉精细尺度的聚落形态，并使用人口与健康调查（DHS）数据集对其输出进行验证，该数据集提供了独立的、基于调查的城乡标签。该模型在大陆尺度上实现了65%的总体准确率和0.47的Kappa系数，其性能优于SMOD等现有全球产品。由此产生的高分辨率城乡（HUR）数据集为人类聚落制图提供了一个开放且可复现的框架，使得对非洲快速演变的聚落系统进行更具情境感知的分析成为可能。我们发布了覆盖2016年至2022年期间的非洲大陆城乡数据集，为非洲的高分辨率聚落制图提供了新的数据源。