Collecting Sidewalk Network Data at Scale for Accessible Pedestrian Travel

Zhang, Yuxiang; Mehta, Sachin; Caspi, Anat

doi:10.1145/3441852.3476560

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies improved pedestrian environment mapping, but they do not generate a comprehensive, connected pedestrian path network needed for city planning and navigation. Other studies use on-the-ground data, such as Zhang et al [29] automated collection of street-view images with auxiliary data to map sidewalk connectivity and sidewalk infrastructure., Hou et al [10] extraction of sidewalks using LiDAR data and point cloud segmentation. However, these methods often require physical systems to cover a large area to generate a pedestrian path network.…”

Section: Pedestrian Environment Mappingmentioning

confidence: 99%

APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks

Zhang¹,

Bolten²,

Mehta³

et al. 2023

Preprint

View full text Add to dashboard Cite

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, 700𝑘𝑚 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.

show abstract

Section: Pedestrian Environment Mappingmentioning

confidence: 99%