About Wang

Recent publications

1. A multi-scale geometric feature-adaptive density-aware framework for robust sub-conductor segmentation in high-voltage transmission corridors

   Yueqian Shen, Chenyang Zhang, Jinhu Wang, Jinguo Wang, Junjun Huang, and Yanming Chen

   Advances in Space Research 2025

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   Fine segmentation of sub-conductors plays a critical role in enabling precise condition assessment for high-voltage transmission corridor inspections. With the widespread applications of bundle conductors in modern power grids, Unmanned Aerial Vehicle Light Detection and Ranging (UAV LiDAR) systems have become indispensable for acquiring sub-conductor point clouds, valued for their operational efficiency and measurement accuracy. However, achieving accurate segmentation in complex power scene point clouds remains challenges due to severe noise interference, high feature similarity among targets, minimal spacing between sub-conductor, and uneven point cloud distribution. To overcome these limitations, this study proposes a novel sub-conductor segmentation algorithm with three core innovations. Firstly, an adaptive multi-scale geometric feature descriptor is designed for the linear span-direction distribution of transmission lines to capture candidate points while mitigating noise interference. Secondly, non-linear mapping strategy using spatial geometric regularities across conductor configurations to reduce dimensionality. Thirdly, an adaptive multi-density perception algorithm is proposed to resolve point cloud density heterogeneity for precise segmentation. Validated across diverse terrains, conductor types, and noise levels, the method achieves an average precision, recall, and F1-score all exceeding 95 %. These results demonstrate its significant potential to advance intelligent inspection capabilities and enhance management efficiency for high-voltage transmission corridors.

   @article{shen2025multi,
   title={A multi-scale geometric feature-adaptive density-aware framework for robust sub-conductor segmentation in high-voltage transmission corridors},
   author={Shen, Yueqian and Zhang, Chenyang and Wang, Jinhu and Wang, Jinguo and Huang, Junjun and Chen, Yanming},
   journal={Advances in Space Research},
   year={2025},
   publisher={Elsevier}
   }
   

2. Boosted bagging: A hybrid ensemble deep learning framework for point cloud semantic segmentation of shield tunnel leakage

   Jundi Jiang, Yueqian Shen, Jinhu Wang, Yufu Zang, Weitong Wu, Jinguo Wang, Junxi Li, and Vagner Ferreira

   Tunnelling and Underground Space Technology 2025

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   Accurate leakage detection in subway shield tunnels remains challenging due to the complex geometry and subtle moisture signatures in structural point clouds. Single-model architectures present limited adaptability to heterogeneous leakage and different tunnel scenarios, leading to substantial performance and compromised generalization capacity. To address these challenges, this research proposes Boosted Bagging, a hybrid ensemble deep learning framework for precise semantic segmentation of leakage in tunnel point clouds. In this method, we propose a boosted learner that integrates three specialized base classifiers to learn complex feature representations of diverse leakage patterns. The boosted learner further synergizes with the bagging strategy to enhance generalization capacity by parallel training on randomized data subsets and voting strategy. Integrating boosting and bagging strategies results in a more precise and robust tunnel leakage segmentation. Moreover, the Lovasz Hinge Loss is introduced to address severe sample imbalance between the leakage and background classes. The experimental results demonstrate the effectiveness of Boosted Bagging in terms of segmentation accuracy and robustness. Comparative experiments with state-of-the-art segmentation methods reveal a notable enhancement in segmentation accuracy. Moreover, its superior performance on test datasets highlights the strong generalization capability of the method.

   @article{jiang2025boosted,
   title={Boosted bagging: A hybrid ensemble deep learning framework for point cloud semantic segmentation of shield tunnel leakage},
   author={Jiang, Jundi and Shen, Yueqian and Wang, Jinhu and Zang, Yufu and Wu, Weitong and Wang, Jinguo and Li, Junxi and Ferreira, Vagner},
   journal={Tunnelling and Underground Space Technology},
   year={2025},
   publisher={Elsevier}
   }
   

3. A workflow for extracting ungulate trails in wetlands using 3D point clouds obtained from airborne laser scanning

   Jinhu Wang, P. Cornelissen, and W. Daniel Kissling

   Frontiers in Remote Sensing 2025

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   Ungulates and other mammalian herbivores can create trails in dense vegetation by trampling and browsing. This can affect vegetation structure and result in the fragmentation of closed, high vegetation, with subsequent impacts on biodiversity. Manually mapping trails in the field or from aerial photographs can be challenging and time-consuming, especially in inaccessible or difficult-to-access habitats such as wetlands and if trails occur beneath the canopy of woody plants (i.e., trees and shrubs) or in other tall vegetation (e.g., reed). Airborne laser scanning (ALS) provides an alternative method because Light Detection and Ranging (LiDAR) can record returns from both the canopy and the ground, as some laser pulses pass through gaps in the vegetation, resulting in highly accurate and dense three-dimensional (3D) point clouds. Here, we present a workflow for extracting ungulate trails using 3D point clouds obtained from country-wide ALS surveys, illustrated by red deer trampling in reedbeds within a 36 km2 marsh area of a Dutch nature reserve. The workflow starts by pre-processing to retile the LiDAR point clouds to designated tiles and removes outliers from the raw data. The (near-)terrain points are then segmented using an iterative filtering algorithm, and digital terrain models are generated with a user-defined resolution. Finally, trail cells are extracted by thresholding the residuals from iterative Laplacian smoothing and then refined by sparse 3D structure tensor voting. The parameters of the workflow were optimized with comprehensive sensitivity analyses. Applying the workflow resulted in a classification of trail and non-trail grid cells at 10 cm resolution across the study area. Compared to manually labeled ground truths, the results showed an overall accuracy of 93% and 90% in regions of red deer grazing only and both geese and red deer grazing, respectively. To test its transferability, the workflow could be applied to other LiDAR data (e.g., ALS surveys from another flight campaign in the same study area or in a different country), to other nature areas (e.g., other rewilding sites or other wetlands), and to other ungulate species (e.g., domesticated livestock or other native large herbivores).

   @article{wang2025workflow,
   title={A workflow for extracting ungulate trails in wetlands using 3D point clouds obtained from airborne laser scanning},
   author={Wang, Jinhu and Cornelissen, Perry and Kissling, W. Daniel},
   journal={Frontiers in Remote Sensing},
   year={2025},
   publisher={Frontiers}
   }