# 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
[PERSON] [219] proposed an automatic method to detect and delineate road junctions from rasterised ALS intensity and normalised DSM in three steps: roughness-enhanced Gabor filters for key point extraction, a higher-order tensor voting algorithm to find the junction candidates, and a geometric template matching to identify the road junction positions and road branch directions. A bridge detection algorithm was proposed by [PERSON] [220]; in this method, DTM cross-sections, that is, profiles, were used to identify bridges. Moreover, forest road detection is possible using detailed ALS DSMs. Road details and complex structures can be extracted from very dense point clouds as well and modelled. In [221], road points were accurately labelled from a dense point cloud of an urban area, using an approximate 2D road network map as the input. They combined both large-scale (snake smoothness) and small-scale (curb detector) cues to extract roads. The method worked on all types of roads, including tunnels, bridges and multilevel intersections. In [222], UAV data was used to perform fine-scale road mapping. [PERSON] [223] studied the automatic extraction of road features (sidewalks, pavement areas and road markings) from high-density ALS point clouds. 3D modelling is required for the most complex parts of the road network such as overpasses and multilevel intersections. The use of LiDAR point clouds to derive 3D city models was reviewed in [77]. [PERSON] [160] studied the detailed 3D reconstruction of multilayer interchange bridges, and satisfactory results were obtained for very complex bridges. In several studies, road detection has been carried out as a part of land cover mapping. Urban land cover mapping by integrating rasterised LiDAR height and intensity data at the object level was proposed by [PERSON] [224]; for this method, an accuracy similar to that of multispectral optical imagery accompanied by ALS DSM was obtained. [PERSON] [225] classified the point cloud into five land cover classes using SVM; during the postprocessing, gaps in the road network were located and filled using a method based on Radon transformation and spline interpolation. Land cover classification using multispectral ALS (MS-ALS) has been the topic of several recently reported studies. Even though a complete road network was not extracted, the road classification results were promising [164; 226]. In [226], the point-based classification completeness for roads was 86% and 92%. In the raster-based classification, accuracies of 92% and 86% were obtained. [PERSON] [227] used rasterised MS-ALS data for a typical road surface (that is, asphalt and gravel) and classified the road types from highways to cycle ways. Due to the lack of shadows, more complete roads (80.5%) were retrieved using this method than using the optical aerial images (71.6%). [PERSON] [228] classified multispectral point clouds into 10 land cover classes using an SVM. Three types of asphalt and concrete classes were included. In general, slightly better results were obtained when the classification was carried out in the point cloud domain; however, the computational costs increased significantly. Deep learning methods for MS-ALS have been explored in a few recent studies. [PERSON] [229] used deep learning-based high-level feature presentation (deep Boltzmann machine) and machine learning methods for land cover classification. [PERSON] [230] proposeda CNN-based classification approach for MS-ALS data. The classification accuracy and computational performance of the constructed CNN model were superior to those of the classical CNN models. [PERSON] [231] proposed a hybrid capsule network using MS-ALS data. The data were rasterised based on the elevation, the number of returns and intensity of the three channels, and an accuracy of 94% was obtained for the road classification. Dense point clouds from SPL were used for land cover classification in [232].