Author ORCID Identifier
https://orcid.org/0000-0002-9951-2019
Document Type
Conference Paper
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences
Abstract
Automatic detection of road intersections is an important task in various domains such as navigation, route planning, traffic prediction, and road network extraction. Road intersections range from simple three-way T-junctions to complex large-scale junctions with many branches. The location of intersections is an important consideration for vulnerable road users such as People with Blindness or Visually Impairment (PBVI) or children. Route planning applications, however, do not give information about the location of intersections as this information is not available at scale. As a first step to solving this problem, a mechanism for automatically mapping road intersection locations is required, ideally using a globally available data source.
DOI
https://doi.org/10.1145/3555776.3578728
Recommended Citation
Eltaher, Fatmaelzahraa; Miralles-Pechuán, Luis; Courtney, Jane; and McKeever, Susan, "Detecting Road Intersections from Satellite Images using Convolutional Neural Networks" (2023). Conference papers. 406.
https://arrow.tudublin.ie/scschcomcon/406
Funder
Science Foundation Ireland
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Publication Details
https://www.researchgate.net/publication/371400239_Detecting_Road_Intersections_from_Satellite_Images_using_Convolutional_Neural_Networks
El-taher, F., Miralles-Pechuan, L. & Courtney, J. (2023). Detecting Road Intersections from Satellite Images using Convolutional Neural Networks, The 38th ACM/SIGAPP Symposium On Applied Computing, Tallinn, Estonia, March 27 - March 31, 2023.
https://doi.org/10.1145/3555776.3578728