CLASSIFICATION OF BRIDGES IN LASER POINT CLOUDS USING MACHINE LEARNING

Sammanfattning: In this work, machine learning was being used for bridge detection in point clouds. To estimate the performance, it was compared to an existing algorithm based on traditional methods for point classification. The purpose of this work was to use machine learning for bridge classification in point clouds. To see how today's machine learning algorithms perform and find the challenges of using machine learning in point classification. The point clouds used are based on airborne laser scanning and represent the land area over Sweden. To get satisfactory results, several different testing areas were used with varying landscapes. For comparing the two different algorithms, both statistical and visual analysis were made to identify the algorithms' behaviours, strengths, and weaknesses. The machine learning algorithm tested was PointNet++, and it was compared to the current algorithm that the Swedish mapping, cadastral and land registration authority use for bridge classification in point clouds. Based on the results, the current method had higher accuracy in the classification of bridge points, but the machine learning approach could detect more bridges. Thus, it was concluded that there are potential for this machine learning approach, but there are still needs for improvements.