Identifying road surface types (paved or unpaved) can ensure road vehicle safety, reduce energy consumption, and promote economic development. Existing studies identified road surface types by using sensors mounted on mobile devices and high-resolution satellite images that are not openly accessible, which makes it difficult to apply them to large-scale (e.g., national or regional) study areas. Addressing this issue, this study developed a dataset of road surface types (paved and unpaved) for the national road network of Kenya, containing 1,267,818 road segments classified as paved or unpaved. To accomplish this, this study proposes a method that integrates crowdsourced geographic data (OpenStreetMap) and Google satellite imagery to identify road surface types. The accuracy, recall, and F1 score of the method were all above 0.94, validating the effectiveness of the method. The data sources of the method are freely available, and the method may be applied to other countries and regions. The dataset developed based on the method can provide data support and decision support for local governments to improve road infrastructure.

识别路面类型（已铺砌或未铺砌）可以确保道路车辆安全、降低能源消耗、促进经济发展。现有研究通过使用安装在移动设备上的传感器和不可公开访问的高分辨率卫星图像来识别道路表面类型，这使得它们难以应用于大规模（例如国家或区域）研究区域。为了解决这个问题，本研究开发了肯尼亚国家道路网的路面类型（已铺砌和未铺砌）数据集，其中包含 1,267,818 个分类为已铺砌或未铺砌的路段。为了实现这一目标，本研究提出了一种集成众包地理数据 (OpenStreetMap) 和谷歌卫星图像来识别路面类型的方法。该方法的准确率、召回率和F1得分均在0.94以上，验证了该方法的有效性。该方法的数据来源是免费的，并且该方法可以应用于其他国家和地区。基于该方法开发的数据集可以为地方政府改善道路基础设施提供数据支持和决策支持。