  Inferring the full transportation network, including sidewalks and cycleways,
is crucial for many automated systems, including autonomous driving,
multi-modal navigation, trip planning, mobility simulations, and freight
management. Many transportation decisions can be informed based on an accurate
pedestrian network, its interactions, and connectivity with the road networks
of other modes of travel. A connected pedestrian path network is vital to
transportation activities, as sidewalks and crossings connect pedestrians to
other modes of transportation. However, information about these paths' location
and connectivity is often missing or inaccurate in city planning systems and
wayfinding applications, causing severe information gaps and errors for
planners and pedestrians. This work begins to address this problem at scale by
introducing a novel dataset of aerial satellite imagery, street map imagery,
and rasterized annotations of sidewalks, crossings, and corner bulbs in urban
cities. The dataset spans $2,700 km^2$ land area, covering select regions from
$6$ different cities. It can be used for various learning tasks related to
segmenting and understanding pedestrian environments. We also present an
end-to-end process for inferring a connected pedestrian path network map using
street network information and our proposed dataset. The process features the
use of a multi-input segmentation network trained on our dataset to predict
important classes in the pedestrian environment and then generate a connected
pedestrian path network. Our results demonstrate that the dataset is
sufficiently large to train common segmentation models yielding accurate,
robust pedestrian path networks.
