SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution
César Aybar, David Montero, Julio Contreras, Simon Donike, Freddie Kalaitzis, Luis Gómez‐Chova
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 exploration of new techniques for enhancing the spatial resolution of Sentinel-2 imagery.