isp-uv-es/SEN2NAIP

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@article{aybar2025sen2naipv2,
  author    = {Aybar, Cesar and Montero, David and Contreras, Julio and Donike, Simon and Kalaitzis, Freddie and Gómez-Chova, Luis},
  title     = {SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution},
  journal   = {Scientific Data},
  year      = {2024},
  volume    = {11},
  number    = {1},
  pages     = {1389},
  doi       = {10.1038/s41597-024-04214-y},
  url       = {https://doi.org/10.1038/s41597-024-04214-y},
  abstract  = {The increasing demand for high spatial resolution in remote sensing has underscored the need for super-resolution (SR) algorithms that can upscale low-resolution (LR) images to high-resolution (HR) ones. To address this, we present SEN2NAIP, a novel and extensive dataset explicitly developed to support SR model training. SEN2NAIP comprises two main components. The first is a set of 2,851 LR-HR image pairs, each covering 1.46 square kilometers. These pairs are produced using LR images from Sentinel-2 (S2) and corresponding HR images from the National Agriculture Imagery Program (NAIP). Using this cross-sensor dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of S2 imagery ($S_{2-like}$). This led to the creation of a second subset, consisting of 35,314 NAIP images and their corresponding $S_{2-like}$ counterparts, generated using the degradation model. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates the exploratio

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signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0