  In our research, an adaptive structural learning method of Restricted
Boltzmann Machine (RBM) and Deep Belief Network (DBN) has been developed as one
of prominent deep learning models. The neuron generation-annihilation in RBM
and layer generation algorithms in DBN make an optimal network structure for
given input during the learning. In this paper, our model is applied to an
automatic recognition method of road network system, called RoadTracer.
RoadTracer can generate a road map on the ground surface from aerial photograph
data. In the iterative search algorithm, a CNN is trained to find network graph
connectivities between roads with high detection capability. However, the
system takes a long calculation time for not only the training phase but also
the inference phase, then it may not realize high accuracy. In order to improve
the accuracy and the calculation time, our Adaptive DBN was implemented on the
RoadTracer instead of the CNN. The performance of our developed model was
evaluated on a satellite image in the suburban area, Japan. Our Adaptive DBN
had an advantage of not only the detection accuracy but also the inference time
compared with the conventional CNN in the experiment results.
