SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution

Journal article

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 (S2like). This led to the creation of a second subset, consisting of 35,314 NAIP images and their corresponding S2like 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.

Authors: Aybar, C; Montero, D; Contreras, J; Donike, S; Kalaitzis, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Data
• Volume: 11
• Issue: 1
• Article number: 1389
• Publication date: 2024-12-18
• Acceptance date: 2024-12-02
• DOI: 10.1038/s41597-024-04214-y
• EISSN: 2052-4463
• ISSN: 2052-4463
• Language: English