Multimodal data fusion of remote sensing and social media using machine learning for natural disaster detection and assessment
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Description
For more than a decade, social media has emerged as a potential platform to generate and spread information about a natural disasters. Alternatively, a common way of conducting research into natural disasters is via remote sensing. However, remote sensing data comes with few challenges, such as, unavailability due to long revisit time of the sensors, and obstruction due to cloud cover. This research develops a novel machine learning algorithm for the fusion of Social media images and text with Remote sensing multispectral satellite images to detect flooding. The fusion method is further extended to generate flood maps for assessment.
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| ID Code: | 238058 | ||
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| Item Type: | QUT Thesis (PhD) | ||
| Supervisor: | Woodley, Alan & Perrin, Dimitri | ||
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| Keywords: | Multimodal Data Fusion, Machine Learning, Direct Backpropagation, Neural Networks, Flood Detection, Social Media, Multilingual, Remote Sensing, Flood Maps, NDWI | ||
| DOI: | 10.5204/thesis.eprints.238058 | ||
| Pure ID: | 125165350 | ||
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science |
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| Institution: | Queensland University of Technology | ||
| Deposited On: | 17 Feb 2023 05:34 | ||
| Last Modified: | 17 Feb 2023 05:34 |
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