Automatically detect crosswalks from satellite view images – A deep learning approach with ground truth verification-Reference-Cited by-同舟云学术

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Automatically detect crosswalks from satellite view images – A deep learning approach with ground truth verification

Published:2024-01 Issue: Volume: Page:
ISSN:2046-0430
Container-title:International Journal of Transportation Science and Technology
language:en
Short-container-title:International Journal of Transportation Science and Technology

Author:

Luttrell Joseph,Zhang Yuanyuan,Zhang Chaoyang

Funder

National Academy of Sciences

Publisher

Elsevier BV

Reference36 articles.

1. Zebra crossing spotter: automatic population of spatial databased for increased safety of blind travelers;Ahmetovic;Assets,2015

2. Mind your crossings: mining GIS imagery for crosswalk localization;Ahmetovic;ACM Trans. Accessible Comput.,2017

3. Automatic large-scale data acquisition via crowdsourcing for crosswalk classification: a deep learning approach;Berriel;Comput. Graph.,2017

4. Deep learning based large-scale automatic satellite crosswalk classification;Berriel;IEEE Geosci. Remote Sens. Lett.,2017

5. Cevallos, F., 2017. Safe and Accessible Pedestrian Facilities Inventory Model (SAPFIM): Planning and Design. National Center for Transit Research.

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