Conference paper Open Access

Multimodal Data Fusion Of Social Media And Satellite Images For Emergency Response And Decision-making

Ilias Gialampoukidis; Stelios Andreadis; Stefanos Vrochidis; Ioannis Kompatsiaris


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/5843802</identifier>
  <creators>
    <creator>
      <creatorName>Ilias Gialampoukidis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5234-9795</nameIdentifier>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Stelios Andreadis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5519-1962</nameIdentifier>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Stefanos Vrochidis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2505-9178</nameIdentifier>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannis Kompatsiaris</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6447-9020</nameIdentifier>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Multimodal Data Fusion Of Social Media And Satellite Images For Emergency Response And Decision-making</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Multimodal data fusion</subject>
    <subject>Social Media</subject>
    <subject>Emergency response</subject>
    <subject>Decision-making</subject>
    <subject>Deep Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-07-11</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5843802</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/IGARSS47720.2021.9554176</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Artificial Intelligence (AI) is already part of our lives and is extensively entering the space sector to offer value-added Earth Observation (EO) products and services. The Copernicus programme provides data on a free, full and open basis, while the recently launched Data and Information Access Service (DIAS) providers index, store and exchange tremendous amounts of data and cloud infrastructure computational resources. Copernicus data and other georeferenced data sources are often highly heterogeneous, distributed and semantically fragmented. One example is the massively generated social media data from citizen observations, including visual, textual and spatiotemporal information. Social media information offers reliable, timely and very prescriptive information about a crisis event. In this work we present the multimodal fusion aspects for combining satellite images and social media for emergency response, such as flood monitoring and extreme weather conditions in polar regions.&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/776019/">776019</awardNumber>
      <awardTitle>EOPEN: opEn interOperable Platform for unified access and analysis of Earth observatioN data</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/101004152/">101004152</awardNumber>
      <awardTitle>Copernicus Artificial Intelligence Services and data fusion with other distributed data sources and processing at the edge to support DIAS and HPC infrastructures</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
36
41
views
downloads
Views 36
Downloads 41
Data volume 54.1 MB
Unique views 27
Unique downloads 37

Share

Cite as