A reliable, punctual, and spatially accurate dataset of sidewalks is vital for identifying where improvements can be made upon urban environment to enhance multi-modal accessibility, social cohesion, and residents' physical activity. This paper develops a synthetically new spatial procedure to extract the sidewalk by integrating the detected results from aerial and street view imagery. We first train neural networks to extract sidewalks from aerial images, and then use pre-trained models to restore occluded and missing sidewalks from street view images. By combining the results from both data sources, a complete network of sidewalks can be produced. Our case study includes four counties in the U.S., and both precision and recall reach about 0.9. The street view imagery helps restore the occluded sidewalks and largely enhances the sidewalk network's connectivity by linking 20% of dangles.

一个可靠，准时和空间精确的人行道数据集对于确定可以改善城市环境的位置以增强多式联运，社会凝聚力和居民的身体活动至关重要。本文通过综合从空中和街景图像中检测到的结果，开发了一种综合性的新空间程序来提取人行道。我们首先训练神经网络从航拍图像中提取人行道，然后使用预先训练的模型从街景图像中恢复被遮挡和丢失的人行道。通过合并来自两个数据源的结果，可以生成一个完整的人行道网络。我们的案例研究包括美国的四个县，准确率和召回率均达到0.9。