{
 "id": "isp-uv-es/SEN2NAIP",
 "owner": "isp-uv-es",
 "name": "SEN2NAIP",
 "downloads": 631,
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 "last_modified": "2024-12-19T09:31:33.000Z",
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 "citation": "@article{aybar2025sen2naipv2,\n  author    = {Aybar, Cesar and Montero, David and Contreras, Julio and Donike, Simon and Kalaitzis, Freddie and Gómez-Chova, Luis},\n  title     = {SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution},\n  journal   = {Scientific Data},\n  year      = {2024},\n  volume    = {11},\n  number    = {1},\n  pages     = {1389},\n  doi       = {10.1038/s41597-024-04214-y},\n  url       = {https://doi.org/10.1038/s41597-024-04214-y},\n  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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 "revision": "79e93461ad93911ebcd5aa0f342376e4c41e8743",
 "stated_evidence": "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/blob/79e93461ad93911ebcd5aa0f342376e4c41e8743/README.md",
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}