Finding Relevant Flood Images on Twitter using Content-based Filters

TL;DR

This paper presents an automatic image filtering method using machine learning to identify relevant flood images on Twitter, significantly improving the retrieval quality over keyword-based methods for disaster assessment.

Contribution

It introduces a content-based filtering approach that analyzes images directly, enhancing the accuracy of relevant flood image retrieval from social media data.

Findings

Image filter improves mean average precision from 23% to 53%.

Content-based filtering outperforms keyword-based filtering.

Method effective across different flooding events.

Abstract

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures. At the same time, a plethora of information is buried in an abundance of images of the event posted on social media platforms such as Twitter. These images could be used to document and rapidly assess the situation and derive proxy-data not available from sensors, e.g., the degree of water pollution. However, not all images posted online are suitable or informative enough for this purpose. Therefore, we propose an automatic filtering approach using machine learning techniques for finding Twitter images that are relevant for one of the following information objectives: assessing the flooded area, the inundation depth, and the degree of water pollution. Instead of relying on textual information present in the tweet, the filter analyzes the image contents directly. We evaluate the performance of two different approaches and various features on a case-study of two major flooding events. Our image-based filter is able to enhance the quality of the results substantially compared with a keyword-based filter, improving the mean average precision from 23% to 53% on average.