{'fraction_non_alphanumeric': 0.02737226277372263, 'fraction_numerical': 0.01824817518248175, 'mean_word_length': 5.162921348314606, '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}}
Material identification using fuzzy-classification of high resolution hyperspectral imagery of an urban area

Remote sensing of urban materials is crucial for urban planning and management. The use of current data on surface materials is fundamental to many aspects of urban planning such as public health monitoring, natural disaster risk management, energy balancing, and more. Often, this type of information is acquired through field surveys; however, this method of data acquisition can be time consuming, tedious, and costly. With advances in remote sensing, especially high-resolution imagery, this information is now increasingly accessible [1]. Our project uses CASI (compact airborne spectrographic imager), an airborne hyperspectral sensor that measures radiation in up to 288 contiguous bands in a spectral range between 365 nm and 1050 nm. As it is an airborne sensor, it also has the advantage of having high spatial resolution as small as 25cm. The CASI data acquired in the summer of 2016 of the island of Montreal (Quebec, Canada), had a resolution of 1m, and contained 96 bands.