01764158

Component

https://annualmeeting.myt...nlineProgram/Details/15715

Large available databases containing the locations of helipads are known to contain more than a few errors. This is caused due to reasons including mistakes when reporting the coordinates, the removal of helipads without notifying those that maintain these databases, and newly built helipads that are not reported. Currently the most used method for verifying the coordinates in these databases is to manually go over these locations and check if the coordinates are accurate however this can be time consuming. A better auditor can be created by using machine learning and available satellite imagery. This auditor was created by training a convolutional neural network that can identify helipads at these coordinates using the available satellite imagery. The trained network was capable of correctly distinguishing between a helipad and non-helipad with an accuracy of 95%. This algorithm will allow for better maintenance of these helipad databases, which will help pilots be able to safely land at their intended destination.

This paper was sponsored by TRB committee AED50 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

TRBAM-21-01973

English

16p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; Photos; References (34) ; Tables

Airstrips; Data quality; Databases; Helicopters; Image analysis; Landing fields; Landsat satellites; Mapping

Aviation; Data and Information Technology; Operations and Traffic Management

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-01973

TRIS, TRB, ATRI

Dec 23 2020 11:21AM