Replication Data for: DEEP LEARNING FOR MULTIPLE-IMAGE SUPER-RESOLUTION OF SENTINEL-2 DATA
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'The dataset contains the data for training and validating deep neural networks that perform multiple-image super-resolution reconstruction. Each high-resolution (HR) patch extracted from 13 Sentinel-2 bands is associated with a set of 9 low-resolution (LR) images with different sub-pixel shifts. The low-resolution patches are simulated by downsampling the high-resolution patch by a factor of 3 (for details, see the referenced paper). The dimensions of the patches are as follows:- for the bands of 10 m GSD: 360x360 pixels (HR) and 120x120 pixels (LR),- for 20 m bands: 180x180 pixels (HR) and 60x60 pixels (LR),- for 60 m bands: 60x60 pixels (HR) and 20x20 pixels (LR)' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Super-resolution reconstruction, satellite imaging, deep learning, multiple-image super-resolution"]} <|eot_id|>
 Here is the Description of a dataset. Description:'The dataset contains the data for training and validating deep neural networks that perform multiple-image super-resolution reconstruction. Each high-resolution (HR) patch extracted from 13 Sentinel-2 bands is associated with a set of 9 low-resolution (LR) images with different sub-pixel shifts. The low-resolution patches are simulated by downsampling the high-resolution patch by a factor of 3 (for details, see the referenced paper). The dimensions of the patches are as follows:- for the bands of 10 m GSD: 360x360 pixels (HR) and 120x120 pixels (LR),- for 20 m bands: 180x180 pixels (HR) and 60x60 pixels (LR),- for 60 m bands: 60x60 pixels (HR) and 20x20 pixels (LR)' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
The dataset contains the data for training and validating deep neural networks that perform multiple-image super-resolution reconstruction. Each high-resolution (HR) patch extracted from 13 Sentinel-2 bands is associated with a set of 9 low-resolution (LR) images with different sub-pixel shifts. The low-resolution patches are simulated by downsampling the high-resolution patch by a factor of 3 (for details, see the referenced paper). The dimensions of the patches are as follows:- for the bands of 10 m GSD: 360x360 pixels (HR) and 120x120 pixels (LR),- for 20 m bands: 180x180 pixels (HR) and 60x60 pixels (LR),- for 60 m bands: 60x60 pixels (HR) and 20x20 pixels (LR)
Super-resolution reconstruction, satellite imaging, deep learning, multiple-image super-resolution