GENCP: GAN-Based Ground Control Point Generation for Satellite Image Georeferencing

Abstract

Remote sensing has become a core technology for environmental and climate monitoring, supported by expanding sensor constellations, advanced processing capabilities, and coordination frameworks established by the European Space Agency (ESA), Global Earth Observation System of Systems (GEOSS), and Committee on Earth Observation Satellites (CEOS). Ensuring consistency across missions requires robust geometric and radiometric calibration and validation. However, traditional reliance on ground control points (GCPs) is limited by sparse global coverage, temporal instability, and dependence on surveyed accuracy. While alternative geospatial datasets, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) point clouds, and vector databases, can serve as references, challenges remain in data access, automation, and cross-sensor applicability. This study proposes a generative adversarial network (GAN)-based approach to generate geometrically consistent image chips from vector maps. Two models were trained at 50 cm and 10 m resolution within the ESA-supported Generative Ground Control Point (GenCP) study, using Sentinel-2 and very-high-resolution RGB imagery. The generated GenCP image chips are evaluated using image similarity (radiometric consistency), as well as geometric and model-performance metrics. The results demonstrate their suitability for automated Cal/Val workflows and their potential as scalable, fit-for-purpose reference datasets.