Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-adde...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/461 |
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| author | Tsung-Chiang Wu Neng-Gang Kuan Wei-Cheng Lu |
| author_facet | Tsung-Chiang Wu Neng-Gang Kuan Wei-Cheng Lu |
| author_sort | Tsung-Chiang Wu |
| collection | DOAJ |
| description | Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management. Commercial software primarily relies on manual classification, which is time-intensive. This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. These equations serve as attribute functions for classifying datasets. Focusing on materials in traditional Minnan architecture on Kinmen Island, controlled indoor experiments and outdoor case studies validate the approach. The results show classification accuracies of 74% for wood, 98% for stone, and 93% for brick, demonstrating this method’s effectiveness in enhancing point cloud data applications and management. |
| format | Article |
| id | doaj-art-93efcff74a4b412ea34080fcec92cd92 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-93efcff74a4b412ea34080fcec92cd922025-01-24T13:49:01ZengMDPI AGSensors1424-82202025-01-0125246110.3390/s25020461Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDARTsung-Chiang Wu0Neng-Gang Kuan1Wei-Cheng Lu2Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanDepartment of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanDepartment of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanGround-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management. Commercial software primarily relies on manual classification, which is time-intensive. This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. These equations serve as attribute functions for classifying datasets. Focusing on materials in traditional Minnan architecture on Kinmen Island, controlled indoor experiments and outdoor case studies validate the approach. The results show classification accuracies of 74% for wood, 98% for stone, and 93% for brick, demonstrating this method’s effectiveness in enhancing point cloud data applications and management.https://www.mdpi.com/1424-8220/25/2/461automated classificationlaser reflectance intensity (I-value)boxplotpolynomial regression curveattribute function equation |
| spellingShingle | Tsung-Chiang Wu Neng-Gang Kuan Wei-Cheng Lu Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR Sensors automated classification laser reflectance intensity (I-value) boxplot polynomial regression curve attribute function equation |
| title | Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR |
| title_full | Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR |
| title_fullStr | Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR |
| title_full_unstemmed | Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR |
| title_short | Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR |
| title_sort | research on the classification of traditional building materials in southern fujian using the reflection intensity values of ground based lidar |
| topic | automated classification laser reflectance intensity (I-value) boxplot polynomial regression curve attribute function equation |
| url | https://www.mdpi.com/1424-8220/25/2/461 |
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