Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.

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Retraction: Li et al. Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 39, doi.org/10.3390/ijgi8010039

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090402 ◽

2019 ◽

Vol 8 (9) ◽

pp. 402

Author(s):

Zhiqiang Li ◽

Chengqi Cheng ◽

Mei-Po Kwan ◽

Xiaochong Tong ◽

Shaohong Tian

Keyword(s):

Random Forest ◽

Point Cloud ◽

Asphalt Pavement ◽

Point Cloud Data ◽

Cloud Data ◽

Pavement Distress ◽

Random Forest Classification ◽

Forest Classification

All authors of the published article [...]

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Cancer disease prediction with support vector machine and random forest classification techniques

2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom) ◽

10.1109/cyberneticscom.2012.6381608 ◽

2012 ◽

Cited By ~ 1

Author(s):

K Ashfaq Ahmed ◽

Sultan Aljahdali ◽

Nisar Hundewale ◽

K Ishthaq Ahmed

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Support Vector ◽

Disease Prediction ◽

Cancer Disease ◽

Classification Techniques ◽

Random Forest Classification ◽

Forest Classification

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Comparison of Classification Methods used in Machine Learning for Dysgraphia Identification

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i11.6142 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1886-1891

Author(s):

Sarthika Dutt, Et. al.

Keyword(s):

Machine Learning ◽

Random Forest ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbors ◽

Feature Selection Technique ◽

Random Forest Classification ◽

Visual Spatial ◽

Forest Classification

Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task. It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naïve Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.

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The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance

Journal of Spectroscopy ◽

10.1155/2018/8316918 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Na’eem Hoosen Agjee ◽

Onisimo Mutanga ◽

Kabir Peerbhay ◽

Riyad Ismail

Keyword(s):

Random Forest ◽

Classification Performance ◽

Machine Learning Algorithms ◽

Support Vector ◽

Random Forest Classification ◽

Node Splitting ◽

Forest Classification ◽

Vector Machines ◽

Classifier Performance ◽

The Impact

Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable noisy conditions, the extent of this is poorly understood. This study aimed to evaluate the influence of simulated spectral noise (10%, 20%, and 30%) on random forest (RF) and oblique random forest (oRF) classification performance using two node-splitting models (ridge regression (RR) and support vector machines (SVM)) to discriminate healthy and low infested water hyacinth plants. Results from this study showed that RF was slightly influenced by simulated noise with classification accuracies decreasing for week one and week two with the addition of 30% noise. In comparison to RF, oRF-RR and oRF-SVM yielded higher test accuracies (oRF-RR: 5.36%–7.15%; oRF-SVM: 3.58%–5.36%) and test kappa coefficients (oRF-RR: 10.72%–14.29%; oRF-SVM: 7.15%–10.72%). Notably, oRF-RR test accuracies and kappa coefficients remained consistent irrespective of simulated noise level for week one and week two while similar results were achieved for week three using oRF-SVM. Overall, this study has demonstrated that oRF-RR can be regarded a robust classification algorithm that is not influenced by noisy spectral conditions.

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