Knapp, Simon Orlando
We present a flexible, automated, Bayesian method designed for
broad scale land use mapping.
The method is based on a Monte Carlo Markov Chain and integrates
a number of sources of
ancillary data. It produces a probability density over a finite
set of land use classes that can be
used directly in further analyses or to classify individual
pixels. The method assumes a multi-
nomial prior over the possible land use types, and uses
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