Journal article Open Access

Multimodal Fusion of Sentinel 1 Images and Social Media Data for Snow Depth Estimation

Damianos Florin Mantsis; Marios Bakratsas; Stelios Andreadis; Petteri Karsisto; Anastasia Moumtzidou; Ilias Gialampoukidis; Ari Karppinen; Stefanos Vrochidis; Ioannis Kompatsiaris


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    <subfield code="a">Sentinel 1 images</subfield>
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    <subfield code="a">Backscatter measurements</subfield>
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    <subfield code="a">Snow depth</subfield>
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    <subfield code="a">Stelios Andreadis</subfield>
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    <subfield code="u">Space and Earth Observation Centre, Finnish Meteorological Institute</subfield>
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    <subfield code="a">Petteri Karsisto</subfield>
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    <subfield code="u">Centre for Research and Technology Hellas - Information Technologies Institute</subfield>
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    <subfield code="a">Anastasia Moumtzidou</subfield>
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    <subfield code="u">Space and Earth Observation Centre, Finnish Meteorological Institute</subfield>
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    <subfield code="a">Ari Karppinen</subfield>
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    <subfield code="u">Centre for Research and Technology Hellas - Information Technologies Institute</subfield>
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    <subfield code="a">Stefanos Vrochidis</subfield>
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    <subfield code="u">Centre for Research and Technology Hellas - Information Technologies Institute</subfield>
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    <subfield code="u">Centre for Research and Technology Hellas - Information Technologies Institute</subfield>
    <subfield code="a">Damianos Florin Mantsis</subfield>
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    <subfield code="a">Multimodal Fusion of Sentinel 1 Images and Social Media Data for Snow Depth Estimation</subfield>
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    <subfield code="a">&lt;p&gt;Recent developments in remote sensing have shown that snow depth can be estimated accurately on a global scale using satellite images through cross-polarization and copolarization backscatter measurements. This method does, however, have some limitations in low-land areas with dense forest coverage and shallow snow, which are often found nearby urban areas. In these areas, citizen observations can be fused with satellite-based estimations to deliver more accurate solutions. To that end, we use snow-related tweets that have been annotated by artificial intelligence (AI) methods and are introduced in a novel neural network model, aiming to increase the estimation accuracy of the state-of-the-art remote sensing method. The proposed model combines the estimated snow depth from Sentinel 1 images with the number of Twitter posts and Twitter images that are semantically relevant to snow. The use of instant social media data for purposes of snow depth estimation is investigated, validated, and tested in Finland. Our results show that this approach does improve the snow depth estimation, highlighting its potential for use in civil protection agencies in managing snow conditions.&lt;/p&gt;</subfield>
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