{'fraction_non_alphanumeric': 0.013100436681222707, 'fraction_numerical': 0.0, 'mean_word_length': 5.457746478873239, '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}}
Recovery Monitoring in Haiti After Hurricane Matthew Through Markov Random Fields and A Region-Based Approach

The monitoring of the recovery phase in the aftermath of an emergency scenario is tackled in this paper in terms of a change-detection perspective and through the integration of multisensor, multisource, and contextual information associated with high resolution optical and SAR data. The method makes use of the Markov random field theory to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows the characterization of the geometrical structures in the images through the employment of multiple segmentation maps at different scales. The performances of the proposed approach are evaluated on pairs of COSMO-SkyMed/Pléiades images acquired over Haiti in the aftermath of Hurricane Matthew.