2D and 3D LiDAR with CNN Models for Detecting Sediment Accumulation Underground after Disasters
| dc.contributor.author | Krungseanmuang, Woranidtha | |
| dc.contributor.author | Morita, Fuka | |
| dc.contributor.author | Chaowalittawin, Vasutorn | |
| dc.contributor.author | Sathaporn, Posathip | |
| dc.contributor.author | Kanamori, Chisato | |
| dc.contributor.author | Archevapanich, Tuanjai | |
| dc.contributor.author | Purahong, Boonchana | |
| dc.date.accessioned | 2026-08-06T10:49:12Z | |
| dc.date.available | 2026-08-06T10:49:12Z | |
| dc.date.issued | 2025-01-01 | |
| dc.description.abstract | Global climate change impacts all regions and leads to natural disasters such as typhoons, which cause destruction, debris, and flooding. Postdisaster restoration is a very important activity that is mostly done manually and can be time-consuming and challenging, especially in subterranean environments owing to accumulated objects such as pipes, pillars, and mud distributed in confined underground areas. Therefore, in this study, we aim to utilize emerging AI technologies by comparing deep learning algorithms and evaluating four models for 2D object detection and four for 3D point cloud segmentation for detecting sediment accumulation and navigating around obstacles in underground areas after a disaster. Additionally, a custom dataset was developed to simulate underground disaster scenarios. As a result, the You Only Look Once version 11 (YOLOv11) model achieved the highest mean average precision 50 (mAP50: 91.1%) for general detection within the pillar-pipe dataset, whereas the YOLOv12 model performed the best in detecting pipes (mAP50: 87.7%). In the mud dataset, the YOLOv8 segmentation (YOLOv8-seg) model demonstrated superior performance with mAP50 scores of 93.0% (detection) and 86.4% (segmentation). For 3D point cloud segmentation, PointNet achieved the highest accuracy (98.61%), whereas RandLA-Net was optimal for pipe segmentation, achieving an intersection over union score of 37.1%. These findings highlight AI’s potential to accelerate disaster recovery, reduce manual labor, and ensure faster cleanup. Integrating deep learning models into post-typhoon restoration efforts can enable communities to recover more quickly and efficiently after climate change impacts or disaster events. | |
| dc.identifier.citation | Sensors and Materials, 37(9), 3855-3867, 2025 | |
| dc.identifier.doi | 10.18494/SAM5713 | |
| dc.identifier.issn | 09144935 | |
| dc.identifier.other | 2-s2.0-105015956834 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/16460 | |
| dc.source | Sensors and Materials | |
| dc.subject | 2D object detection | |
| dc.subject | 3D point cloud data | |
| dc.subject | CNN | |
| dc.subject | disaster | |
| dc.subject | semantic segmentation | |
| dc.subject | YOLO | |
| dc.title | 2D and 3D LiDAR with CNN Models for Detecting Sediment Accumulation Underground after Disasters | |
| dc.type | Article |