Building A Deep Learning Model for Multi-Label Classification of Natural Disasters
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. |
| Pages | 505-509 |
| ISBN (electronic) | 978-1-6654-9079-5 |
| ISBN (print) | 978-1-6654-9080-1 |
| Publication status | Published - 2023 |
Publication series
| Name | Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA |
|---|
Conference
| Title | 3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA 2023) |
|---|---|
| Place | China |
| City | Chongqing |
| Period | 26 - 28 May 2023 |
Link(s)
Abstract
Natural disasters, such as earthquakes, hurricanes/typhoons and wildfires, usually cause severe damage. Disaster response and management is a great challenge to the authority. Current studies usually focus on a single disaster identification using social media data. In reality, there are relationships among different types of disasters. And several disasters may happen simultaneously. In this study, we explore the role of the deep learning model in multi-label disaster classification. We build a deep CNN model for multi-label classification with the instruction of a high-order strategy. We train and validate our model using a professional low-altitude disaster dataset, LADI. We find our proposed deep learning model with the transfer learning method outperforms many other machine learning models in the previous study. © 2023 IEEE.
Research Area(s)
- deep learning, disaster classification, multi-label learning
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Building A Deep Learning Model for Multi-Label Classification of Natural Disasters. / Cao, Qiang; Liu, Yan; Wang, Guangxu et al.
2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 505-509 (Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA).
2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 505-509 (Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
