An Enhanced Gan Model For Automatic Satellite-To-Map Image Conversion

Location-based service significantly relies on accurate and up-to-date maps. The conventional map generation involves labor-intensive and time-consuming manual efforts, which restricts the map-update frequency to a few years or even longer. In recent years, satellite images become more ubiquitous, and converting them to map-style images has attracted attention due to its frequent-updating and cost-effective nature. Generative adversarial network (GAN) is a promising approach for automatic satellite-to-map image conversion. However, it is still challenging to process satellite images when the underlying road structure is complex and irregular, or when some objects are visually indistinguishable due to obstruction or bad weather. To address these issues, we propose an enhanced GAN model to generate improved quality map images by bringing in the external geographic data as implicit guidance. The textual geographic data is converted to an image so that it can work collaboratively and seamlessly with the satellite image during the conversion. A high-level semantic regulation is also introduced to further reduce the noisy patterns generated during the translation, which occur frequently for the regions with sparse geographic data. The proposed method is versatile to various backbone GAN structures with a 20% performance improvement on three popular metrics (Inception Score, Frechet Inception Distance Score and SSIM score). Our proposed Semantic-regulated Geographic GAN (SG-GAN) is anticipated to reduce the manual identification efforts in broad geospatial applications.