Landslide Detection in Real-Time Social Media Image Streams
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| _version_ | 1866909179219804160 |
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| author | Ofli, Ferda Imran, Muhammad Qazi, Umair Roch, Julien Pennington, Catherine Banks, Vanessa J. Bossu, Remy |
| author_facet | Ofli, Ferda Imran, Muhammad Qazi, Umair Roch, Julien Pennington, Catherine Banks, Vanessa J. Bossu, Remy |
| contents | Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. However, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence (AI) techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real time. To that end, we create a large landslide image dataset labeled by experts and conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_04080 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Landslide Detection in Real-Time Social Media Image Streams Ofli, Ferda Imran, Muhammad Qazi, Umair Roch, Julien Pennington, Catherine Banks, Vanessa J. Bossu, Remy Computer Vision and Pattern Recognition Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. However, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence (AI) techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real time. To that end, we create a large landslide image dataset labeled by experts and conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response. |
| title | Landslide Detection in Real-Time Social Media Image Streams |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2110.04080 |