  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.
