## 4 Discussion  
A study was conducted to produce 2.5 m and 5.0 m resolution imagery with 10 m Sentinel-2 satellite images using aerial orthoimage as a ground truth. For this, training samples were produced by acquiring Sentinel-2 satellite images and aerial orthoimages over the same area and period. The training samples were used to simulate 2.5 m and 5.0 m aerial orthoimages. For quality check and general applicability of our neural network, additional test samples in an independent region were utilized. For producing better-simulated images, a new context-based neural network was proposed and compared with the existing neural network. Our context-based ResU-Net generally outperformed the baseline and EDSR for all three metrics, both in training samples and test samples. We believe that this is because conv-depth blocks helped the stability of our model. In any case, the utility of our model for successfully predicting narrow roads will be very high. Meanwhile, in order to improve the performance compared to the present, the obstacles to be solved were speculated as follows:  
First, the effect of shadows in HR aerial images was significant. The Sentinel-2 images were acquired with a low-resolution at high altitude, whereas aerial images were acquired with HR at low altitude. Even in the same area, when images were acquired at a low altitude, the effect of shadows was much more prominent than at a high altitude. Because most of our study area included urban landscapes, the effect of shadows on HR images was much greater than for high altitude images. The original 51 cm aerial orthoimage was resampled to obtain GT using bilinear interpolation. During the bilinear interpolation process to create GT from the original 51 cm aerial orthoimage, the effect shadow smeared into other features and worsened the SSIM metric.  
Second, there existed the effects of color correction during the composition of aerial orthoimages. The primary purpose of aerial orthoimages distributed by the Korean government is to produce a visually attractive map for the general public. We speculated that the original reflectance information had been corrected to make the orthoimage more pleasing, leading to potentially difficult and inaccurate training due to the use of aerial orthoimages.  
For this study, it is essential that both aerial orthoimages and satellite images must be taken at a similar period of time. Recently, some countries have provided aerial orthoimages, so if researchers can check the acquisition date of aerial orthoimages, we expect that our research results can be utilized.  
In future research, steps for shadow identification and shadow removal must be included based on deep learning, especially when using the HR aerial images as training sets. In the remote sensing community, CNN-based SR research is ongoing. However, several studies have tried to combine images obtained from multiple sensors to produce new images. We believe that the method and results presented in this study can contribute new insights for researchers performing similar studies.  
[PERSON] and [PERSON] were the leading directors of this research. [PERSON] designed the overall research plan and programmed them for experiments, while [PERSON] programmed training data generation and augmentation. [PERSON] and [PERSON] performed the preprocessing of aerial and satellite images. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (no. 20009742) from the Disaster-Safety Industry Promotion Program funded by the Ministry of Interior and Safety (MOIS, Korea).  
Not applicable.  
The authors declare no conflict of interest.