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

TL;DR

This paper introduces a large-scale annotated dataset combining satellite imagery and street maps to improve the automatic inference of connected pedestrian path networks in urban areas, aiding transportation planning and navigation.

Contribution

The work presents a novel dataset and an end-to-end segmentation approach for accurately mapping pedestrian networks from aerial and street imagery.

Findings

The dataset covers 2,700 km^2 across 6 cities.

Segmentation models trained on this dataset produce accurate pedestrian networks.

The approach enhances urban transportation mapping and planning.

Abstract

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.