Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis
When a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2454526 |
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| author | Sebastian Schmidt Monika Friedemann David Hanny Bernd Resch Torsten Riedlinger Martin Mühlbauer |
| author_facet | Sebastian Schmidt Monika Friedemann David Hanny Bernd Resch Torsten Riedlinger Martin Mühlbauer |
| author_sort | Sebastian Schmidt |
| collection | DOAJ |
| description | When a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis products to emergency managers, this paper proposes a geo-social media-based approach that detects disaster events based on the spatio-temporal analysis of georeferenced, disaster-related Tweets. The proposed methodology is validated on the basis of two use cases: wildfires in Chile and British Columbia. The results show the general ability of Twitter to forecast events several days in advance, at least for the Chile use case. However, there are large spatial differences, as there is a correlation between population density and the reliability of Twitter data. Consequently, only few meaningful alerts could be generated for British Columbia, an area with very low population numbers. |
| format | Article |
| id | doaj-art-0134fac6dd204cfc96438352a9e226d5 |
| institution | Kabale University |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-0134fac6dd204cfc96438352a9e226d52025-01-30T09:41:15ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-01-0112310.1080/20964471.2025.2454526Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysisSebastian Schmidt0Monika Friedemann1David Hanny2Bernd Resch3Torsten Riedlinger4Martin Mühlbauer5Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, AustriaGeo-Risks and Civil Security, German Aerospace Center (DLR), Weßling, GermanyDepartment of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, AustriaDepartment of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, AustriaGeo-Risks and Civil Security, German Aerospace Center (DLR), Weßling, GermanyGeo-Risks and Civil Security, German Aerospace Center (DLR), Weßling, GermanyWhen a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis products to emergency managers, this paper proposes a geo-social media-based approach that detects disaster events based on the spatio-temporal analysis of georeferenced, disaster-related Tweets. The proposed methodology is validated on the basis of two use cases: wildfires in Chile and British Columbia. The results show the general ability of Twitter to forecast events several days in advance, at least for the Chile use case. However, there are large spatial differences, as there is a correlation between population density and the reliability of Twitter data. Consequently, only few meaningful alerts could be generated for British Columbia, an area with very low population numbers.https://www.tandfonline.com/doi/10.1080/20964471.2025.2454526Disaster alertssatellite-based emergency mappingTwitterRoBERTa |
| spellingShingle | Sebastian Schmidt Monika Friedemann David Hanny Bernd Resch Torsten Riedlinger Martin Mühlbauer Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis Big Earth Data Disaster alerts satellite-based emergency mapping RoBERTa |
| title | Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis |
| title_full | Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis |
| title_fullStr | Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis |
| title_full_unstemmed | Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis |
| title_short | Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis |
| title_sort | enhancing satellite based emergency mapping identifying wildfires through geo social media analysis |
| topic | Disaster alerts satellite-based emergency mapping RoBERTa |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2025.2454526 |
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