# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 5 Conclusions  
High-resolution and hyperspectral images have been widely used in digital road network extraction. More satellites with high spatial resolution and short revisit periods are being developed and launched, promoting the development of heuristic and data-driven road extraction methods. However, few of these hyperspectral satellite data can be used for road extraction. Therefore, airborne systems are still the main approach for the acquisition of hyperspectral data [80,252]. Data-driven methods have high accuracy and show significant potential; however, transfer learning needs to be improved. In addition, the combination of high-resolution image data with other data sources such LiDAR is one feasible approach to solve some challenging issues such as occlusion or shadows.  
Large areas can be rapidly mapped using weather-independent spaceborne SAR images; for example, images acquired after sudden changes in the target area. In addition, global datasets enable global mapping. However, the roads are challenging to interpret because of the interaction of the signal with the surrounding areas. ALS provides excellent data for the generation of topographic databases and detailed mapping of limited areas. Multispectral ALS may be the best remote sensing data source for road mapping; however, only small areas are covered at a time in this case. Further studies are required for developing sensors for various landscapes and road types; moreover, fully automated road detection is still in its infancy.  
High-resolution imaging, hyperspectral imaging, SAR imaging, and LiDAR are currently the primary techniques for road extraction. As shown in Table 8, different data sources have unique characteristics. For example, high-resolution images have high spatial resolution and contain rich textures, shapes, structures, and neighbourhood relations of ground objects. Hyperspectral images have multiple data dimensions and rich spectral features. In addition, SAR imaging and LiDAR are less affected by external environmental factors such as clouds, fog, and light and can operate in all weather conditions. Combining different remote sensing data to use their respective advantages is a notable approach for developing advanced road extraction methods in the future.  
**Author Contributions:** Bibliographic review, J.J., H.S., C.J., K.K. and M.K.; paper organisation, Y.C. and J.J.; drawing of conclusions, J.J.; writing--original draft preparation, J.J., H.S., C.J., K.K. and M.K.; writing--review and editing, E.A., E.K., P.H., C.C. and T.X.; supervision, Y.C.; project administration, J.H.; funding acquisition, Y.C. and T.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by Academy of Finland projects \"Ultrafast Data Production with Broadband Photodetectors for Active Hyperspectral Space Imaging (No. 336145)\", Forest-Human-Machine Interplay-Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE), (No. 337656) and Strategic Research Council project \"Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing-Point Cloud Ecosystem (No. 314312). Additionally, the Chinese Academy of Science (No. 181811 KYSB20160040 XDA22030202), Shanghai Science and Technology Foundations (No. 18590712600) and Jihua lab (No. X190211 TE190) and Huawei (No. 9424877) are acknowledged.  
**Institutional Review Board Statement:** Not applicable.