[Viper :: Machine Learning, Data Mining and Information Retrieval]

Measuring the Impact of Natural Hazards with Citizen Science: The Case of Flooded Area Estimation Using Twitter

Bibtex entry :

@article { bruneau:remotesensing2021,
    author = { Pierrick Bruneau and Etienne Brangbour and Stephane Marchand-Maillet and Renaud Hostache and Marco Chini and Ramona Pelich and Patrick Matgen and Thomas Tamisier },
    title = { Measuring the Impact of Natural Hazards with Citizen Science: The Case of Flooded Area Estimation Using Twitter },
    journal = { Remote. Sens. },
    volume = { 13 },
    number = { 6 },
    pages = { 1153 },
    year = { 2021 },
    url = { https://doi.org/10.3390/rs13061153 },
}

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion