Accurate Land Use and Land Cover (LULC) maps are essential for understanding the drivers of sustainable development, in terms of its complex interrelationships between human activities and natural resources. However, existing LULC maps often lack precise urban and rural classifications, particularly in diverse regions like Africa. This study presents a novel construction of a high-resolution rural-urban map using deep learning techniques and satellite imagery. We developed a deep learning model based on the DeepLabV3 architecture, which was trained on satellite imagery from Landsat-8 and the ESRI LULC dataset, augmented with human settlement data from the GHS-SMOD. The model utilizes semantic segmentation to classify land into detailed categories, including urban and rural areas, at a 10-meter resolution. Our findings demonstrate that incorporating LULC along with urban and rural classifications significantly enhances the model's ability to accurately distinguish between urban, rural, and non-human settlement areas. Therefore, our maps can support more informed decision-making for policymakers, researchers, and stakeholders. We release a continent wide urban-rural map, covering the period 2016 and 2022.

本研究解决了现有土地利用和土地覆盖（LULC）地图在非洲等多样化地区缺乏精确城市与乡村分类的问题。我们采用基于DeepLabV3架构的深度学习模型，利用Landsat-8卫星影像和增强的人类定居点数据，构建了一个10米分辨率的高分辨率城乡地图。研究结果表明，结合LULC与城乡分类的信息显著提高了识别城市、乡村及非人类定居区的准确性，进而为决策者和利益相关者提供更为有效的信息支持。

非洲定居点映射：通过深度学习和卫星影像生成高分辨率的城市和乡村地图