# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 4 Combination of Multisource Data for Road Extraction
### Some Scopes of Future Research in Road Extraction  
A summary of road extraction based on different data sources is presented in Table 8. Some prospects can be derived based on the status of current road extraction from high-resolution images. First, data-driven road extraction methods exhibit excellent performance and high extraction accuracy [34]. Therefore, more robust data-driven methods should be developed and verified using different datasets. In addition, it is difficult to obtain high detection accuracy using only one algorithm in some cases; therefore, a combination of multiple road extraction methods must be studied [247]. Finally, the combination of data sources (e.g., hyperspectral, SAR and LiDAR) should be evaluated further. Spatial resolution is one of the most important factors affecting the performance of road extraction methods. High-spatial-resolution images can describe fine objects in detail. However, this increases the spectral variability within the class [248]. [PERSON] et al. [249] demonstrated that images with a spatial resolution of 0.5 m had higher accuracy than those with a spatial resolution of 0.1 m. Data-driven methods show higher road extraction accuracy with improved spatial resolution than do the traditional heuristic methods [34; 71].  
It is necessary to continue to study road extraction from airborne hyperspectral images [80]. In addition, new road extraction methods based on hyperspectral images should be developed. In particular, the advantages of a large number of bands and continuous spectrum coverage of hyperspectral images should be further exploited, and new data-driven methods should be proposed [250]. Finally, hyperspectral image datasets with a wide coverage area and road labels should be produced to promote the road extraction application of hyperspectral data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & \begin{tabular}{c} **Resolution/** \\ **Mapping Unit** \\ \end{tabular} & **Extent** & **Advantages** & **Roads Extended Mostly by** \\ \hline \begin{tabular}{c} High spatial resolution \\ [71], 2020 \\ \end{tabular} & 0.5–10 m & Local/regional/global & \begin{tabular}{c} Most tools available, \\ “basic” software \\ \end{tabular} & \begin{tabular}{c} Colour, texture \\ Colour, texture and spectral \\ features \\ Linear features/edge \\ \end{tabular} \\ \
\begin{tabular}{c} AIS [75], 2017 \\ \end{tabular} & 0.25–2 m & Local (nationwide) & Height information &
\begin{tabular}{c} 3D geometry \\ (intensity) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of road extraction based on different data sources.  
Recently, the open-access global SAR satellite datasets (Sentinel-1) have enabled the mapping and monitoring of large areas. However, the resolution of this method is limited. Microsatellite constellations can acquire large amounts of very-high-resolution data with higher frequency; however, this aspect has not been studied in detail. For ALS, deep learning methods [251] for image classification are rapidly emerging, followed by methods for earth observation. Publicly available open training datasets acquired by the earth observation satellite are being used for road extraction. The highest accuracies for road classification have been reported using deep neural network algorithms. However, these studies cover limited areas where they perform relatively well, but no large-scale tests have been conducted yet.