  Disaster analysis in social media content is one of the interesting research
domains having abundance of data. However, there is a lack of labeled data that
can be used to train machine learning models for disaster analysis
applications. Active learning is one of the possible solutions to such problem.
To this aim, in this paper we propose and assess the efficacy of an active
learning based framework for disaster analysis using images shared on social
media outlets. Specifically, we analyze the performance of different active
learning techniques employing several sampling and disagreement strategies.
Moreover, we collect a large-scale dataset covering images from eight common
types of natural disasters. The experimental results show that the use of
active learning techniques for disaster analysis using images results in a
performance comparable to that obtained using human annotated images, and could
be used in frameworks for disaster analysis in images without tedious job of
manual annotation.
