# Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
## 2 Materials and Methods
### Methodology
#### 2.2.1 Interactive Single-Road Extraction
Earth Obs. Remote Sens._**2012**, \(5\), 1538-1544. [CrossRef]
* [10] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A new approach to urban road extraction using high-resolution aerial image. _ISPRS Int. J. Geo-Inf._**2016**, \(5\), 114. [CrossRef]
* [11] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image. _Remote Sens. Environ._**2021**, _259_, 112412. [CrossRef]* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] DeepRoadMapper: Extracting Road Topology from Aerial Images. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22-29 October 2017; pp. 3458-3466. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] Semi-automatic Road Extraction from SAR Images Using EKF and PF. _Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci._**2015**, _XL-7/W4_, 227-230. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Road marking extraction in UAV imagery using attentive capsule feature pyramid network. _Int. J. Appl. Earth Obs._**2022**, _107_, 102677. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON]; [PERSON] Automatic road extraction from historical maps using deep learning techniques: A regional case study of turkey in a German World War II Map. _ISPRS Int. J. Geo-Inf._**2021**, _10_, 492. [CrossRef]
* [PERSON] (2021) [PERSON]; [PERSON] Road characteristics detection based on joint convolutional neural networks with adaptive squares. _ISPRS Int. J. Geo-Inf._**2021**, _10_, 377. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] SDUNet: Road extraction via spatial enhanced and densely connected UNet. _Pattern Recognit._**2022**, _126_, 108549. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Fully Convolutional Network-Based Ensemble Method for Road Extraction from Aerial Images. _IEEE Geosci. Remote Sens. Lett._**2020**, _17_, 1777-1781. [CrossRef]
* [PERSON] (2020) [PERSON]; [PERSON] Deep learning for geometric and semantic tasks in photogrammetry and remote sensing. _Goo-Spat. Inf. Sci._**2020**, _23_, 10-19. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Road extraction from very high resolution images using weakly labeled openstreetmap centerline. _ISPRS Int. J. Geo-Inf._**2019**, \(8\), 478. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Semi automatic road extraction from digital images. _Egypt. J. Remote Sens. Space Sci._**2017**, _20_, 117-123. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] A semi-automatic method for road centerline extraction from VHR images. _IEEE Geosci. Remote Sens. Lett._**2014**, _11_, 1856-1860. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]. Semi-automatic road network extraction from digital images using object-based classification and morphological operators. _Bol. Cienc. Geod._**2018**, _24_, 485-502. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Feature detection and description for image matching: From hand-crafted design to deep learning. _Geo-Spat. Inf. Sci._**2020**, _24_, 58-74. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A review of deep learning methods for semantic segmentation of remote sensing imagery. _Expert Syst. Appl._**2021**, _169_, 114417. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] RDD2020: An annotated image dataset for automatic road damage detection using deep learning. _Data Brief_**2021**, _36_, 107133. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al.