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

TL;DR

This paper introduces a novel Teacher-Student adaptive deep belief network model to improve automatic road network extraction from aerial images, demonstrating significant accuracy gains and applicability to disaster response scenarios.

Contribution

The paper proposes a new ensemble learning approach using adaptive DBN with neuron and layer generation for enhanced road detection accuracy.

Findings

Detection accuracy improved from 40% to 89%.

Model successfully applied to landslide-affected road detection.

Lightweight implementation enables rapid inference on embedded devices.

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 RBM and layer generation algorithm 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. A novel method of RoadTracer using the Teacher-Student based ensemble learning model of Adaptive DBN is proposed, since the 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 the 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 available roads when landslide by natural disaster is occurred, in order to rapidly obtain a way of transportation. For fast inference, a small size of the trained model was implemented on a small embedded edge device as lightweight deep learning. We reported the detection results for the satellite image before and after the rainfall disaster in Japan.