# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on LiDAR Data  
#### 3.4.1 Main Methods  
Ground points and nonground points can be easily distinguished based on pulse and elevation information in airborne LiDAR data. To classify ground and road/nonroad areas, intensity information is needed. Roads, such as water surfaces, have low intensity. In addition to the surface reflectance, the intensity values are affected by atmospheric attenuation, transmitted power, detection range and incidence angle. Therefore, calibration is required to apply the methods over large areas. Road detection using intensity is usually based on the surface homogeneity and consistency of roads. The LiDAR-based methods for road extraction either use point cloud processing and classification or are based on a digital surface model (DSM), digital terrain model (DTM) and intensity raster produced from the point clouds. In some reported studies, the main focus was on data classification, while in others, a complete road network was extracted. Point cloud-based automatic road extraction using LiDAR height and intensity was first proposed by [PERSON] [213]; in this study, a hierarchical classification method was used to classify point clouds progressively into roads and nonroads. Individual points were selected based on the height difference to generate a DTM, and the intensity value was obtained via filtering based on point density and morphological filtering of a binary image. The method was enhanced in [214], where a phase-coded disk algorithm was introduced to vectorise the binary road network image. [PERSON] [215] proposed a method for road centreline extraction using the salient linear features observed in the images of complex urban areas; in this method, tensor voting was used to eliminate the nonroad areas. Intensity variations in the road network were considered in [216], where road points were selected from ground points based on a local intensity distribution histogram and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Category** & **Characteristic** & **References** & **Precision** \\ \hline Multiple Detectors & Heuristic & Fusion of different pre-processing algorithms, road extractors & [202], 2003 & 0.580 \\ Line based on vector Radon transform Multitemporal InSAR covariance and & Heuristic & Suitable for different platform SAR images & [209], 2019 & 0.700–0.940 correctness \\ information fusion & Heuristic & Use interferometric information & [210], 2017 & 0.816 \\ FCN-based & Data-driven & Automatic road extraction & [158], 2019 & 0.921 \\ FCN-8s & Data-driven & Lack efficiency & [46], 2018 & 0.717 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of road extraction using SAR images. filtered by roughness and area. [PERSON] [217] extracted road centrelines using three steps; a skewness-balancing algorithm was proposed to obtain the intensity threshold. A rotating neighbourhood algorithm was proposed to extract the main roads (by removing the narrow roads), and a hierarchical fusion and optimisation algorithm was proposed to extract the road network. For the three test sites, correctness values of 43-92% and completeness of 36-91% were obtained. A raster-based road extraction method for grid-structured urban areas was proposed by [PERSON] [218]; in this study, the ground objects were classified, road centrelines were extracted using a total least square line fitting approach, and a voting-based road direction was used to evaluate each road segment's reliability by removing areas such as parking lots from the road segments. For a complete road network extraction, different parts of the road network such as junctions and bridges need to be considered as well.