Automatic Extraction of Road Networks by using Teacher-Student Adaptive Structural Deep Belief Network and Its Application to Landslide Disaster
arXiv:2511.05567v2 Announce Type: replace-cross 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 metho
The continuous development in deep learning models and computational capabilities enables more sophisticated applications in computer vision for infrastructure mapping.
Precise and automated road network extraction is crucial for disaster response, urban planning, and autonomous systems, potentially accelerating infrastructure development and recovery efforts.
The efficiency and accuracy of mapping critical infrastructure like road networks can be significantly improved through advanced AI models, reducing manual labor and response times.
- · Disaster relief organizations
- · Urban planners
- · Mapping software developers
- · Autonomous vehicle companies
- · Traditional manual mapping services
Automated accurate road network maps become more readily available for various applications.
Improved infrastructure planning and faster disaster response enhance societal resilience and economic efficiency.
The widespread adoption of such AI systems could lead to new standards for mapping and urban development, integrating real-time data seamlessly.
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