用1亿张街景图构建全球道路铺装数据集，助力城市规划与可持续发展。
Paved or unpaved? A Deep Learning derived Road Surface Global Dataset from Mapillary Street-View Imagery
- 融合SWIN Transformer与CLIP-DL模型，自动识别道路铺装类型并过滤低质图像。
- 覆盖超300万公里道路，占全球路网约36%，城市铺装率超80%。
- 数据开源，适用于交通、物流、灾害响应等场景，支持多个可持续发展目标。
我们基于全球最大众包街景平台Mapillary的1.05亿张图像，利用先进的地理空间AI方法，发布了首个全球覆盖的道路铺装状态数据集（铺装或非铺装）。提出一种混合深度学习方法：采用SWIN-Transformer进行道路铺装预测，结合CLIP与深度学习分割技术实现低质量图像过滤。预测结果与OpenStreetMap（OSM）道路几何信息匹配整合。研究提供了地图与统计数据，涵盖大陆及国家尺度的铺装分布情况，区分城乡差异。该数据集将全球道路铺装信息扩展超过300万公里，现代表约36%的全球道路网络。多数地区铺装覆盖率在60%-80%之间，但非洲和亚洲部分区域存在显著缺口。城市地区铺装覆盖率接近100%，农村则差异较大。模型在与OSM表面数据对比中表现优异，跨大陆铺装道路F1得分达91%-97%。本工作延续Mapillary贡献者成果，丰富了OSM道路属性，为城市规划、灾害应急路径、物流优化提供支持，并助力多个可持续发展目标（如减贫、健康、基础设施、气候行动等）。
原文摘要 · Abstract (English)
We have released an open dataset with global coverage on road surface characteristics (paved or unpaved) derived utilising 105 million images from the world's largest crowdsourcing-based street view platform, Mapillary, leveraging state-of-the-art geospatial AI methods. We propose a hybrid deep learning approach which combines SWIN-Transformer based road surface prediction and CLIP-and-DL segmentation based thresholding for filtering of bad quality images. The road surface prediction results have been matched and integrated with OpenStreetMap (OSM) road geometries. This study provides global data insights derived from maps and statistics about spatial distribution of Mapillary coverage and road pavedness on a continent and countries scale, with rural and urban distinction. This dataset expands the availability of global road surface information by over 3 million kilometers, now representing approximately 36% of the total length of the global road network. Most regions showed moderate to high paved road coverage (60-80%), but significant gaps were noted in specific areas of Africa and Asia. Urban areas tend to have near-complete paved coverage, while rural regions display more variability. Model validation against OSM surface data achieved strong performance, with F1 scores for paved roads between 91-97% across continents. Taking forward the work of Mapillary and their contributors and enrichment of OSM road attributes, our work provides valuable insights for applications in urban planning, disaster routing, logistics optimisation and addresses various Sustainable Development Goals (SDGS): especially SDGs 1 (No poverty), 3 (Good health and well-being), 8 (Decent work and economic growth), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable cities and communities), 12 (Responsible consumption and production), and 13 (Climate action).
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