TY - GEN
T1 - Identifying disaster damage images using a domain adaptation approach
AU - Li, Xukun
AU - Caragea, Cornelia
AU - Caragea, Doina
AU - Imran, Muhammad
AU - Ofli, Ferda
N1 - Publisher Copyright:
© 2019 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters, in real time, are greatly needed. While many studies have focused on filtering textual information, the research on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster. To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks (DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone, and uses the adversarial training to find a transformation that makes the source and target data indistinguishable. Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better results as compared to the VGG-19 model fine-tuned on the source labeled data.
AB - Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters, in real time, are greatly needed. While many studies have focused on filtering textual information, the research on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster. To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks (DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone, and uses the adversarial training to find a transformation that makes the source and target data indistinguishable. Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better results as compared to the VGG-19 model fine-tuned on the source labeled data.
KW - Disaster damage
KW - Domain adaptation
KW - Domain adversarial neural networks
KW - Image classification
UR - http://www.scopus.com/inward/record.url?scp=85077742192&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85077742192
T3 - Proceedings of the International ISCRAM Conference
SP - 633
EP - 645
BT - ISCRAM 2019 - Proceedings
A2 - Franco, Zeno
A2 - Gonzalez, Jose J.
A2 - Canos, Jose H.
PB - Information Systems for Crisis Response and Management, ISCRAM
T2 - 16th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2019
Y2 - 19 May 2019 through 22 May 2019
ER -