Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning

ISPRS International Journal of Geo-Information

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