Automatic Extraction of Road Networks by using Teacher-Student Adaptive Structural Deep Belief Network and Its Application to Landslide Disaster

Abstract--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 algorithm in R BM and layer generation algorithm in DBN make an optimal networ k 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 . A novel method of RoadTracer using the T eacher-Student base d ensemble learning model of Adaptive DBN is proposed, since t he road maps contain many complicated features so that a model with high representation power to detect should be required . The experimental results showed the detection accuracy of t he proposed model was improved from 40.0% to 89.0% on average in the seven major cities among the test dataset. In addition, we challenged to apply our method to the detection of availab le roads when landslide by natural disaster is occurred, in ord er to rapidly obtain a way of transportation. For fast inferenc e, a small size of the trained model was implemented on a small embedded edge device as lightweight deep learning. Recently there have been more cases of extreme climate events including unexpected and unusual weather. The atten - tion of these events has been received in the last few years, d ue to the significant loss of human lives and escalating economi c costs, as well as the impacts on landslides and changes in ecosystems. In Japan, the Japan Meteorological Agency (JMA) has issued "Climate Change Monitoring Report" every year informing the latest status of climate change. According to [1 ], during the Heavy Rain Event of July 2018, Japan experienced unprecedented heavy rainfall. Overall precipitation obse rved at AMeDAS stations throughout Japan in July 2018 was extremely high in comparison with past heavy rainfall event s since 1982. A prominent characteristic of this rain event is that the record-breaking local precipitation, particularly wi thin 48 to 72 hours, was observed extensively over western Japan and Tokyo region, including the Seto Inland Sea side of Chugoku and Shikoku regions. S. Kamada is with Hiroshima City University, Hiroshima, Jap an T. Ichimura is with Prefectural University of Hiroshima, Hi roshima, Japan In addition, lifelines such as wat er supply and communications damaged, and traffic obstacles occurred over a wide area. Due to the disruption of major roads and railroads, the supply was also suspended.