AI-based 2-m super-resolution of Sentinel-2 imagery for high-resolution SDG monitoring

High-resolution Earth observation (EO) is critical for tracking spatially heterogeneous sustainable development goals (SDGs), such as cropland dynamics and urban expansion. However, persistent limitations in spatiotemporal continuity (satellite revisit gaps) and cost-efficiency (prohibitive pricing of commercial <2 m data) hinder its scalability. While deep learning-based single image super-resolution (SR) techniques offer a potential solution, their quantitative equivalence to native high-resolution data and the generalizability across geographies remain unproven. Here, we demonstrate that AI-powered SR can systematically transform freely available 10-m Sentinel-2 imagery with visible (RGB) and near infrared (NIR) bands into 2m-resolution images with RGB-NIR bands while preserving spectral-temporal fidelity. Specifically, we trained a geospatially constrained transformer-based SR framework (GeoSR) with 3.15 million km² of co-registered Gaofen-1/6 and Sentinel-2 pairs, achieving near-native performance with <1% F1-score loss in critical applications: cultivated land parcels mapping (F1-score = 0.84 vs. 0.85 for native Gaofen-1/6), urban footprint extraction (0.82 vs. 0.81), and fine-grained land use and land cover classification (0.43 vs. 0.43). Notably, the geospatial module enables large-scale generalization, retrospectively reconstructing decade-long environmental dynamics from historical archives–an unprecedented capability unattainable solely through launching new satellites. We further demonstrated operational scalability through two large-scale implementations: mapping 6.09 million hectares of agricultural parcels of the entire Anhui Province, China, and extracting 9866 km² of building footprints across 87 C40 coastal cities. As a free-to-access platform (www.sr-earth.org), GeoSR significantly reduces high-resolution data costs compared to commercial alternatives. AI-powered SR is not merely an image enhancement tool but a scientifically valid EO data source, particularly transformative for specific SDG monitoring in low/middle-income regions where native HR data scarcity impedes evidence-based policymaking.