{'fraction_non_alphanumeric': 0.02702702702702703, 'fraction_numerical': 0.013513513513513514, 'mean_word_length': 5.142857142857143, '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}}
Geospatial 2D and 3D object-based classification and 3D reconstruction of ISO-containers depicted in a LiDAR data set and aerial imagery of a harbor

Within the 2015 IEEE GRSS Data Fusion Contest, an extremely high-resolution 3D LiDAR point cloud of a harbor test site must be “fused” with a 2D multi-spectral aerial image, featuring no radiometric calibration metadata file, of the same surface area. In this scenario we propose an innovative geospatial 2D and 3D object-based classification system, capable of counting instances of two populations of ISO-containers, whose standard dimensions are known a priori based on the ISO 668 - Series 1 freight containers documentation, detected in the 2D and 3D datasets at hand. The degree of novelty of the proposed classification system is twofold. First, it combines inductive (bottom-up, data-driven) and deductive (top-down, prior knowledge-based) inference mechanisms, where the latter initializes the former in a hybrid inference framework. Second, it is provided with feedback loops, which increase its robustness to changes in input data and augment its degree of automation. The geospatial outcome consists of tangible vector objects, which allow estimation of statistics per container together with a detailed reconstruction of the 3D scene in a geographic information system.