A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation

González-Collazo, Silvia María; Balado, Jesús; González, Elena; Nurunnabi, Abdul

doi:10.1016/j.eswa.2023.120672

Cited by 5 publications

(1 citation statement)

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“…The quality of the annotation is a determining factor for a good dataset, as well as for all the successive tasks. As shown by Silvia et al (2023)'s results in [71], the annotation differs from one person to another, even for the same scene and with the same class definition. This has a significant impact on the results of deep learning semantic segmentation models, with a relationship of R 2 = 0.765 between the points labelled in concordance and the F1-score.…”

Section: Data Annotationmentioning

confidence: 84%

Multi-Context Point Cloud Dataset and Machine Learning for Railway Semantic Segmentation

Kharroubi,

Ballouch,

Hajji

et al. 2024

Infrastructures

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Railway scene understanding is crucial for various applications, including autonomous trains, digital twining, and infrastructure change monitoring. However, the development of the latter is constrained by the lack of annotated datasets and limitations of existing algorithms. To address this challenge, we present Rail3D, the first comprehensive dataset for semantic segmentation in railway environments with a comparative analysis. Rail3D encompasses three distinct railway contexts from Hungary, France, and Belgium, capturing a wide range of railway assets and conditions. With over 288 million annotated points, Rail3D surpasses existing datasets in size and diversity, enabling the training of generalizable machine learning models. We conducted a generic classification with nine universal classes (Ground, Vegetation, Rail, Poles, Wires, Signals, Fence, Installation, and Building) and evaluated the performance of three state-of-the-art models: KPConv (Kernel Point Convolution), LightGBM, and Random Forest. The best performing model, a fine-tuned KPConv, achieved a mean Intersection over Union (mIoU) of 86%. While the LightGBM-based method achieved a mIoU of 71%, outperforming Random Forest. This study will benefit infrastructure experts and railway researchers by providing a comprehensive dataset and benchmarks for 3D semantic segmentation. The data and code are publicly available for France and Hungary, with continuous updates based on user feedback.

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Section: Data Annotationmentioning

confidence: 84%