OMRoadNet: A Self-Training-Based UDA Framework for Open-Pit Mine Haul Road Extraction from VHR Imagery

OMRoadNet: A Self-Training-Based UDA Framework for Open-Pit Mine Haul Road Extraction from VHR Imagery

Accurate extraction of dynamically evolving haul roads in open-pit mines from very-high-resolution (VHR) satellite imagery remains a critical challenge due to domain gaps between urban and mining environments, prohibitive annotation costs, and morphological irregularities. This paper introduces OMRo...

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Bibliographic Details
Main Authors: Suchuan Tian, Zili Ren, Xingliang Xu, Zhengxiang He, Wanan Lai, Zihan Li, Yuhang Shi
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
mining road topology modeling
unsupervised domain adaptation (UDA)
haul road dynamic mapping
AI-driven mine infrastructure digitization
adversarial learning for geospatial intelligence
Online Access:https://www.mdpi.com/2076-3417/15/12/6823
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