Summary of Ape: An Open and Shared Annotated Dataset For Learning Urban Pedestrian Path Networks, by Yuxiang Zhang et al.
APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks
by Yuxiang Zhang, Nicholas Bolten, Sachin Mehta, Anat Caspi
First submitted to arxiv on: 4 Mar 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for inferring a comprehensive transportation network, including sidewalks and cycleways, by leveraging aerial satellite imagery, street map data, and rasterized annotations. The authors introduce a large-scale dataset covering over 2,700 km² of urban areas from six cities, which can be used to segment and understand pedestrian environments. To generate a connected pedestrian path network map, the paper presents an end-to-end process that employs a multi-input segmentation network trained on the proposed dataset. This approach yields accurate and robust results, demonstrating its potential for various applications such as autonomous driving, trip planning, and mobility simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how to create a complete map of cities’ pedestrian paths and connections. The paper uses special images taken from satellites and maps to make a huge dataset that can be used to teach computers about these paths. This is important for things like self-driving cars, planning trips, and making cities more walkable. By using this data and a special computer program, the researchers show how they can create accurate maps of pedestrian networks. |
