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

• URL: http://arxiv.org/abs/2303.02323v1
• Date: Sat, 4 Mar 2023 05:08:36 GMT
• Title: APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks
• Authors: Yuxiang Zhang, Nicholas Bolten, Sachin Mehta, Anat Caspi
• Abstract summary: Inferring the full transportation network, including sidewalks and cycleways, is crucial for many automated systems. This work begins to address this problem at scale by introducing a novel dataset of aerial satellite imagery, map imagery, and annotations of sidewalks, crossings, and corner bulbs in urban cities. We present an end-to-end process for inferring a connected pedestrian path network map using street network information and our proposed dataset.
• Score: 16.675093530600154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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