  Unstructured data from diverse sources, such as social media and aerial
imagery, can provide valuable up-to-date information for intelligent situation
assessment. Mining these different information sources could bring major
benefits to applications such as situation awareness in disaster zones and
mapping the spread of diseases. Such applications depend on classifying the
situation across a region of interest, which can be depicted as a spatial
"heatmap". Annotating unstructured data using crowdsourcing or automated
classifiers produces individual classifications at sparse locations that
typically contain many errors. We propose a novel Bayesian approach that models
the relevance, error rates and bias of each information source, enabling us to
learn a spatial Gaussian Process classifier by aggregating data from multiple
sources with varying reliability and relevance. Our method does not require
gold-labelled data and can make predictions at any location in an area of
interest given only sparse observations. We show empirically that our approach
can handle noisy and biased data sources, and that simultaneously inferring
reliability and transferring information between neighbouring reports leads to
more accurate predictions. We demonstrate our method on two real-world problems
from disaster response, showing how our approach reduces the amount of
crowdsourced data required and can be used to generate valuable heatmap
visualisations from SMS messages and satellite images.
