利用深度学习和卫星影像制作非洲高分辨率城乡地图
A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery
AI总结：
本研究利用深度学习和卫星影像，开发了高分辨率非洲城乡地图，提升了对非洲聚居地变化的分析能力。
摘要：
准确且一致地绘制城乡区域对于可持续发展、空间规划和政策设计至关重要。特别是在模拟人类活动与自然资源之间的复杂相互作用方面尤为重要。现有的全球城乡数据集，如GHSL-SMOD、GHS城市化程度和GRUMP，往往空间分辨率低、方法不一致且难以适应如非洲这样的异质地区，限制了它们在政策和研究中的实用性。它们的粗略网格和基于规则的分类方法会掩盖小型或非正式聚居地，并导致国家之间的一致性问题。在本研究中，我们开发了一个基于DeepLabV3的深度学习框架，整合了多源数据，包括Landsat-8影像、VIIRS夜间灯光、ESRI土地利用土地覆盖（LULC）和GHS-SMOD，以产生2016年至2022年间非洲大陆的10米分辨率城乡地图。使用Landsat数据也突显了将此制图方法扩展到历史上的潜力，可以追溯到1990年代。该模型利用语义分割来捕捉精细的聚居地形态，其输出通过人口与健康调查（DHS）数据集进行验证，该数据集提供了独立的、基于调查的城乡标签。该模型在大陆层面实现了65%的整体准确率和0.47的Kappa系数，优于现有的全球产品如SMOD。所得到的高分辨率城乡（HUR）数据集提供了一个开放且可重复的框架，用于制图人类聚居地，使更具有情境意识地分析非洲快速演变的聚居系统成为可能。我们发布了覆盖2016至2022年的大陆范围城乡数据集，提供了一个新的高分辨率非洲聚居地制图来源。
英文摘要：
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.
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