  After a natural disaster, such as a hurricane, millions are left in need of
emergency assistance. To allocate resources optimally, human planners need to
accurately analyze data that can flow in large volumes from several sources.
This motivates the development of multimodal machine learning frameworks that
can integrate multiple data sources and leverage them efficiently. To date, the
research community has mainly focused on unimodal reasoning to provide granular
assessments of the damage. Moreover, previous studies mostly rely on
post-disaster images, which may take several days to become available. In this
work, we propose a multimodal framework (GaLeNet) for assessing the severity of
damage by complementing pre-disaster images with weather data and the
trajectory of the hurricane. Through extensive experiments on data from two
hurricanes, we demonstrate (i) the merits of multimodal approaches compared to
unimodal methods, and (ii) the effectiveness of GaLeNet at fusing various
modalities. Furthermore, we show that GaLeNet can leverage pre-disaster images
in the absence of post-disaster images, preventing substantial delays in
decision making.
