ISPRS International Journal of Geo-Information | |
Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning | |
MichaelE. Hodgson1  Cuizhen(Susan) Wang1  Huan Ning1  Zhenlong Li1  | |
[1] Department of Geography, University of South Carolina, Columbia, SC 29208, USA; | |
关键词: twitter; flood; real-time; image; deep learning; | |
DOI : 10.3390/ijgi9020104 | |
来源: DOAJ |
【 摘 要 】
This article aims to implement a prototype screening system to identify flooding-related photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decision-makers. This screening system, designed for application to social media images, includes several key modules: tweet/image downloading, flooding photo detection, and a WebGIS application for human verification. In this study, a training dataset of 4800 flooding photos was built based on an iterative method using a convolutional neural network (CNN) developed and trained to detect flooding photos. The system was designed in a way that the CNN can be re-trained by a larger training dataset when more analyst-verified flooding photos are being added to the training set in an iterative manner. The total accuracy of flooding photo detection was 93% in a balanced test set, and the precision ranges from 46−63% in the highly imbalanced real-time tweets. The system is plug-in enabled, permitting flexible changes to the classification module. Therefore, the system architecture and key components may be utilized in other types of disaster events, such as wildfires, earthquakes for the damage/impact assessment.
【 授权许可】
Unknown