ACCURACY ASSESSMENT OF A SLAM-ACQUIRED POINT CLOUD DATA USING A VARIETY OF CLASSIFICATION APPLICATIONS

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-4/W8-2023; pp. 307 - 312
• Main Authors: Ingles, H. A., Legaspi, A. M. M., Sarmiento, C. J. S., Claridades, A. R. C.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 25.04.2024; Copernicus Publications
• Subjects: Accuracy; Applications programs; Classification; Comparative analysis; Data acquisition; Recall; Simultaneous localization and mapping; Three dimensional models
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-4-W8-2023-307-2024

Laser scanning techniques, such as Simultaneous Localization and Mapping (SLAM), produce three-dimensional data representing the real world, which may provide significant information for Building Information Models (BIM). These processes produce 3D point clouds, which require classification before being used in various applications such as structural assessments. However, most widely available software applications for classifying 3D point clouds are proprietary, giving an incomplete depiction of how the data is manipulated and processed. Thus, this research aims to assess the accuracy of the different classification applications in classifying the 3D point cloud data and perform a comparative analysis of the results. Precision, Recall, F1-score, and Accuracy are the evaluation metrics used to assess the classified 3D point cloud data. Results for Precision and Recall show that some of the applications can classify a particular class, the Ground and Building classes. However, the overall performance of the classification method, which is evaluated through the F1-score, produced low values. Results for the F1-score demonstrate that these low values indicate low overall reliability of the classification results despite high values for Accuracy. Based on the conducted experiments, further research is suggested to investigate the effect of increasing dataset size and equalizing class sizes used in classification.