{'fraction_non_alphanumeric': 0.028029678483099753, 'fraction_numerical': 0.0008244023083264633, 'mean_word_length': 5.8977272727272725, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An interactive visual analytic tool for semantic classification of 3D urban LiDAR point cloud

We propose a novel unsupervised machine learning approach for effective semantic labeling by combining two different multi-class classifications, structural and contextual classification, of points in airborne LiDAR point cloud of urban environment. Structural classification labels a point in the cloud as a point-, line-, or surface-type feature. An additional outcome of this classification is the geometry-preserving downsampling of the point cloud. The contextual classification, on the other hand, labels the points in four classes, namely, buildings, vegetation, natural ground, and asphalt ground, by using data derived from the raw input, which includes the structural classification. Preserving these two classifications in the labeling of the points gives a geometry-aware contextual semantic labeling. We propose: (a) an augmented semantic classification which preserves both structural and contextual classification, (b) an interactive hierarchical clustering method for contextual classification, and (c) an interactive visual analytic framework to aid both the structural and contextual classifications.