{'fraction_non_alphanumeric': 0.02, 'fraction_numerical': 0.005, 'mean_word_length': 5.307086614173229, '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}}
Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This article presents a deep-learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24-kn root mean squared error. In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.