Table 3 UAV-driven methods for civil infrastructure condition assessment
Application | Data Analytics | Literature | |
|---|---|---|---|
Structural damage assessment | • Image-based 3D reconstruction • Image segmentation and object classification for damage feature extraction | ||
• Machine learning-based classification of damaged buildings using feature sets obtained from feature extraction and transformation in images | (Ye et al. 2014) | ||
Infrastructure inspection | • Image-based 3D reconstruction • Geometrical feature recognition and classification for planning laser scans | (Oskouie et al. 2015) | |
• Creating comprehensive, high-resolution, semantically rich 3D models of infrastructure | (ARIA (Team 2015)) | ||
Urban monitoring | • Image-based 3D reconstruction for inferring geometric characteristics of buildings • Segmentation using geometric features obtained from 3D point cloud along with radiometric features | (Vetrivel 2999) | |
• 4D image registration for change detection • Orthophoto mapping and multi-primitive image segmentation for object-based decision tree analysis | (Qin 2014) | ||
Road Assessment | • Image-based 3D reconstruction | • Feature extraction through image filtering | (Dobson et al. 2013) |
• Analyzing the size and dimension of road surface distresses • Feature extraction and Orthophoto mapping | (Zhang & Elaksher 2012) | ||
Surveying | • Image-based 3D reconstruction • Surveying post-disaster sites | (Yamamoto et al. 2014) | |
Solar power plant investigations | • Leveraging aerial triangulation using ImageStation Automatic Triangulation (ISAT) software | (Matsuoka et al. 2012) | |
Geo-hazard investigations | • Orthophoto mapping and visual interpretation to inspect geologic hazards along oil and gas pipelines | (Gao et al. 2011) | |