Articles producció científicaGeografia

VGI and Satellite Imagery Integration for Crisis Mapping of Flood Events

  • Dades identificatives

    Identificador:  imarina:9290172
    Autors:  Vavassori, Alberto; Carrion, Daniela; Zaragozi, Benito; Migliaccio, Federica
    Resum:
    Timely mapping of flooded areas is critical to several emergency management tasks including response and recovery activities. In fact, flood crisis maps embed key information for an effective response to the natural disaster by delineating its spatial extent and impact. Crisis mapping is usually carried out by leveraging data provided by satellite or airborne optical and radar sensors. However, the processing of these kinds of data demands experienced visual interpretation in order to achieve reliable results. Furthermore, the availability of in situ observations is crucial for the production and validation of crisis maps. In this context, a frontier challenge consists in the use of Volunteered Geographic Information (VGI) as a complementary in situ data source. This paper proposes a procedure for flood mapping that integrates VGI and optical satellite imagery while requiring limited user intervention. The procedure relies on the classification of multispectral images by exploiting VGI for the semi-automatic selection of training samples. The workflow has been tested with photographs and videos shared on social media (Twitter, Flickr, and YouTube) during two flood events and classification consistency with reference products shows promising results (with Overall Accuracy ranging from 87% to 93%). Considering the limitations of social media-sourced photos, the use of QField is proposed as a dedicated application to collect metadata needed for the image classification. The research results show that the integration of high-quality VGI data and semi-automatic data processing can be beneficial for crisis map production and validation, supporting crisis management with up-to-date maps.
  • Altres:

    Autor segons l'article: Vavassori, Alberto; Carrion, Daniela; Zaragozi, Benito; Migliaccio, Federica
    Departament: Geografia
    Autor/s de la URV: Zaragozí Zaragozí, Benito Manuel
    Paraules clau: Crisis map; Flood detection; Optical satellite imagery; Qfield; Semi-automatic processing; Social media; Vgi; Volunteered geographic information; Water
    Resum: Timely mapping of flooded areas is critical to several emergency management tasks including response and recovery activities. In fact, flood crisis maps embed key information for an effective response to the natural disaster by delineating its spatial extent and impact. Crisis mapping is usually carried out by leveraging data provided by satellite or airborne optical and radar sensors. However, the processing of these kinds of data demands experienced visual interpretation in order to achieve reliable results. Furthermore, the availability of in situ observations is crucial for the production and validation of crisis maps. In this context, a frontier challenge consists in the use of Volunteered Geographic Information (VGI) as a complementary in situ data source. This paper proposes a procedure for flood mapping that integrates VGI and optical satellite imagery while requiring limited user intervention. The procedure relies on the classification of multispectral images by exploiting VGI for the semi-automatic selection of training samples. The workflow has been tested with photographs and videos shared on social media (Twitter, Flickr, and YouTube) during two flood events and classification consistency with reference products shows promising results (with Overall Accuracy ranging from 87% to 93%). Considering the limitations of social media-sourced photos, the use of QField is proposed as a dedicated application to collect metadata needed for the image classification. The research results show that the integration of high-quality VGI data and semi-automatic data processing can be beneficial for crisis map production and validation, supporting crisis management with up-to-date maps.
    Àrees temàtiques: Biodiversidade; Ciência da computação; Ciências agrárias i; Ciências ambientais; Computer science, information systems; Computers in earth sciences; Earth and planetary sciences (miscellaneous); Engenharias i; Geociências; Geografía; Geography, physical; Geography, planning and development; Medicina veterinaria; Remote sensing; Saúde coletiva; Zootecnia / recursos pesqueiros
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: benito.zaragozi@urv.cat
    Data d'alta del registre: 2025-02-24
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2220-9964/11/12/611
    Referència a l'article segons font original: Isprs International Journal Of Geo-Information. 11 (12): 611-
    Referència de l'ítem segons les normes APA: Vavassori, Alberto; Carrion, Daniela; Zaragozi, Benito; Migliaccio, Federica (2022). VGI and Satellite Imagery Integration for Crisis Mapping of Flood Events. Isprs International Journal Of Geo-Information, 11(12), 611-. DOI: 10.3390/ijgi11120611
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.3390/ijgi11120611
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: info:eu-repo/semantics/article
  • Paraules clau:

    Computer Science, Information Systems,Computers in Earth Sciences,Earth and Planetary Sciences (Miscellaneous),Geography, Physical,Geography, Planning and Development,Remote Sensing
    Crisis map
    Flood detection
    Optical satellite imagery
    Qfield
    Semi-automatic processing
    Social media
    Vgi
    Volunteered geographic information
    Water
    Biodiversidade
    Ciência da computação
    Ciências agrárias i
    Ciências ambientais
    Computer science, information systems
    Computers in earth sciences
    Earth and planetary sciences (miscellaneous)
    Engenharias i
    Geociências
    Geografía
    Geography, physical
    Geography, planning and development
    Medicina veterinaria
    Remote sensing
    Saúde coletiva
    Zootecnia / recursos pesqueiros
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