  The analysis of natural disasters such as floods in a timely manner often
suffers from limited data due to a coarse distribution of sensors or sensor
failures. This limitation could be alleviated by leveraging information
contained in images of the event posted on social media platforms, so-called
"Volunteered Geographic Information (VGI)". To save the analyst from the need
to inspect all images posted online manually, we propose to use content-based
image retrieval with the possibility of relevance feedback for retrieving only
relevant images of the event to be analyzed. To evaluate this approach, we
introduce a new dataset of 3,710 flood images, annotated by domain experts
regarding their relevance with respect to three tasks (determining the flooded
area, inundation depth, water pollution). We compare several image features and
relevance feedback methods on that dataset, mixed with 97,085 distractor
images, and are able to improve the precision among the top 100 retrieval
results from 55% with the baseline retrieval to 87% after 5 rounds of feedback.
