Learning Representations from Road Network for End-to-End Urban Growth
  Simulation
  From our experiences in the past, we have seen that the growth of cities is
very much dependent on the transportation networks. In mega cities,
transportation networks determine to a significant extent as to where the
people will move and houses will be built. Hence, transportation network data
is crucial to an urban growth prediction system. Existing works have used
manually derived distance based features based on the road networks to build
models on urban growth. But due to the non-generic and laborious nature of the
manual feature engineering process, we can shift to End-to-End systems which do
not rely on manual feature engineering. In this paper, we propose a method to
integrate road network data to an existing Rule based End-to-End framework
without manual feature engineering. Our method employs recurrent neural
networks to represent road networks in a structured way such that it can be
plugged into the previously proposed End-to-End framework. The proposed
approach enhances the performance in terms of Figure of Merit, Producer's
accuracy, User's accuracy and Overall accuracy of the existing Rule based
End-to-End framework.
