TY - GEN
T1 - A Hybrid Transfer Learning Approach to Migratable Disaster Assessment in Social Media Sensing
AU - Zhang, Yang
AU - Zong, Ruohan
AU - Wang, Dong
N1 - Funding Information:
This research is supported in part by the National Science Foundation under Grant No. CNS-1845639, CNS-1831669, Army Research Office under Grant W911NF-17-1-0409. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Social media sensing has emerged as a powerful sensing paradigm to collect the observations of the physical world by exploring the 'wisdom of crowd'. In this paper, we focus on a migratable disaster damage assessment problem in social media sensing applications. Our goal is to accurately identify the damage severity of affected areas in an unfolding disaster event using unlabeled social media data feeds (e.g., image posts on social media). Two fundamental challenges exist in solving our problem: i) different disaster events often have distinct characteristics (e.g., damage types, affected areas) that cannot be easily migrated; ii) it is non-Trivial to modify a damage assessment model from a previous event to adapt to a new event without using the labeled data from the new event. To address the above challenges, we develop SocialTrans, a hybrid deep transfer learning framework, to enable effective model migration for accurate damage assessment without using any training data from the studied disaster event. The evaluation results on four real-world disaster events show that SocialTrans consistently outperforms the state-of-The-Art baselines in accurately assessing the damage level of disasters.
AB - Social media sensing has emerged as a powerful sensing paradigm to collect the observations of the physical world by exploring the 'wisdom of crowd'. In this paper, we focus on a migratable disaster damage assessment problem in social media sensing applications. Our goal is to accurately identify the damage severity of affected areas in an unfolding disaster event using unlabeled social media data feeds (e.g., image posts on social media). Two fundamental challenges exist in solving our problem: i) different disaster events often have distinct characteristics (e.g., damage types, affected areas) that cannot be easily migrated; ii) it is non-Trivial to modify a damage assessment model from a previous event to adapt to a new event without using the labeled data from the new event. To address the above challenges, we develop SocialTrans, a hybrid deep transfer learning framework, to enable effective model migration for accurate damage assessment without using any training data from the studied disaster event. The evaluation results on four real-world disaster events show that SocialTrans consistently outperforms the state-of-The-Art baselines in accurately assessing the damage level of disasters.
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U2 - 10.1109/ASONAM49781.2020.9381433
DO - 10.1109/ASONAM49781.2020.9381433
M3 - Conference contribution
AN - SCOPUS:85103695395
T3 - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
SP - 131
EP - 138
BT - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
A2 - Atzmuller, Martin
A2 - Coscia, Michele
A2 - Missaoui, Rokia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Y2 - 7 December 2020 through 10 December 2020
ER -