  In all types of disasters, from earthquakes to armed conflicts, aid workers
need accurate and timely data such as damage to buildings and population
displacement to mount an effective response. Remote sensing provides this data
at an unprecedented scale, but extracting operationalizable information from
satellite images is slow and labor-intensive. In this work, we use machine
learning to automate the detection of building damage in satellite imagery. We
compare the performance of four different convolutional neural network models
in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify
how well the models will generalize to future disasters by training and testing
models on different disaster events.
