The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1
score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1
score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1
score of 74.8% for ALS data classification while using the ground truth training samples the average F1
score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs.
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