Details
| Original language | English |
|---|---|
| Article number | 39 |
| Journal | ISPRS International Journal of Geo-Information |
| Volume | 7 |
| Issue number | 2 |
| Early online date | 25 Jan 2018 |
| Publication status | Published - Feb 2018 |
Abstract
In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens' safety. Therefore, real-Time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin.
Keywords
- Convolutional neural network, Crowdsourcing, Flood mapping, Multimedia information retrieval, Social media, Transfer learning, Volunteered geographic information, Word embedding
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
Sustainable Development Goals
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In: ISPRS International Journal of Geo-Information, Vol. 7, No. 2, 39, 02.2018.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos
AU - Feng, Yu
AU - Sester, Monika
N1 - Funding information: Acknowledgments: The authors would like to acknowledge the support from BMBF funded research project “EVUS — Real-Time Prediction of Pluvial Floods and Induced Water Contamination in Urban Areas” (BMBF, 03G0846A). We also gratefully acknowledge the support of NVIDIA Corporation with the donation of a GeForce Titan X GPU used for this research. The publication of this article was funded by the Open Access Fund of the Leibniz Universität Hannover.
PY - 2018/2
Y1 - 2018/2
N2 - In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens' safety. Therefore, real-Time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin.
AB - In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens' safety. Therefore, real-Time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin.
KW - Convolutional neural network
KW - Crowdsourcing
KW - Flood mapping
KW - Multimedia information retrieval
KW - Social media
KW - Transfer learning
KW - Volunteered geographic information
KW - Word embedding
UR - http://www.scopus.com/inward/record.url?scp=85043230454&partnerID=8YFLogxK
U2 - 10.3390/ijgi7020039
DO - 10.3390/ijgi7020039
M3 - Article
AN - SCOPUS:85043230454
VL - 7
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
SN - 2220-9964
IS - 2
M1 - 39
ER -