mlsanityData to 7 Oct 2026

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

arXiv 2511.05567 ↗First posted 4 Nov 2025cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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. This version of the article was improved the search algorithm at the border around image.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 54 references that are in the map. First posted on arXiv 4 Nov 2025.

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19 of 54 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014Going Deeper with Convolutionscited by 4,231
  4. 2016Wide Residual Networkscited by 3,057
  5. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  6. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  7. 2013Maxout Networkscited by 186
  8. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  9. 2022UIU-Net: U-Net in U-Net for Infrared Small Object Detectioncited by 124
  10. 2020Graph Convolutional Networks for Hyperspectral Image Classificationcited by 113
  11. 2018RoadTracer: Automatic Extraction of Road Networks from Aerial Imagescited by 102
  12. 2014Fractional Max-Poolingcited by 35
7 more
  1. 2022Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classificationcited by 25
  2. 2022Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classificationcited by 20
  3. 2019RoadTagger: Robust Road Attribute Inference with Graph Neural Networkscited by 11
  4. 2018An Adaptive Learning Method of Deep Belief Network by Layer Generation Algorithmcited by 6
  5. 2018An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithmcited by 5
  6. 2021Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Networkcited by 1
  7. 2021An Embedded System for Image-based Crack Detection by using Fine-Tuning model of Adaptive Structural Learning of Deep Belief Networkcited by 1

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