Semantic Segmentation of Golf Courses for Course Rating Assistance

Jesper Kjærgaard Mortensen, Vinicius Soares Matthiesen, Jacobo González de Frutos, Kata Bujdosó, Jesper Thøger Christensen, Andreas Møgelmose

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This paper introduces a system to assist golf course raters in determining the difficulty rating of a golf hole. Currently, determining the rating of a given golf hole relies on time-consuming manual measurements on the ground, which we attempt to partially automate. A U - net neural network is trained to classify greens, fairways, tees, bunkers, and water in golf courses, and a course rating assistance system is implemented to measure distances between relevant course parts. Since no public datasets containing golf courses existed prior to this work, we present a new public data set of golf courses created from orthophotos. 1,123 RGB orthophotos for training/validation and 108 RGB orthophotos for testing were gathered from 107 Danish golf courses (58 % of Danish courses) during the spring season and manually annotated. The dataset is publicly available on Kaggle 1 1 The U-net model accomplished a mean intersection over union (IoU) of 69.6%, mean sensitivity of 78.0%, and mean positive predictive value (PPV) of 84.1 %. Based on this automatic analysis of the course images, the course rating assistance system computes 5 crucial distances for course raters to determine a course rating and achieved a mean error of 2.7% and 17.7 % for green length and width, as well as a mean error of 3.3 % and 4.2 % for male/female hole lengths.
TitelProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
Antal sider6
Publikationsdato23 aug. 2023
ISBN (Trykt)979-8-3503-1316-1
ISBN (Elektronisk)979-8-3503-1315-4
StatusUdgivet - 23 aug. 2023
BegivenhedIEEE International Conference on Multimedia and Expo - Brisbane, Australien
Varighed: 10 jul. 202314 jul. 2023


KonferenceIEEE International Conference on Multimedia and Expo


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