GeoMapGAN: Remote Sensing Image to Map via Two‐Stream Network and Adaptive Noise

ABSTRACT As a vital medium for geospatial visualization, map tiles play a crucial role in modern spatial data services. Traditional map tile generation methods mainly rely on vector data and involve complex cartographic workflows, which are computationally intensive and inefficient, making it challenging to meet the demands of real‐time updates. In recent years, the automatic conversion of remote sensing imagery to map tiles using deep learning has become a research hotspot. Although existing methods can effectively improve the efficiency of map generation, they still face challenges in maintaining geometric structure and achieving style transfer. For instance, some generated map tiles perform poorly in preserving map features and struggle to balance global consistency with the fidelity of local details. To address these issues, we propose a novel intelligent online map generation framework named GeoMapGAN, which integrates deep learning‐powered geospatial analysis for end‐to‐end map tile generation from remote sensing data. This approach significantly reduces the reliance on intermediate vector data, thereby enhancing both the efficiency and accuracy of the generation process. In this framework, we employ a dual‐stream encoder to extract features from content and style images separately. A style encoding projection module is used to encode the style image and map it into a specific space, forming a controllable noise vector to guide the generation process. To further enhance the structural accuracy and visual fidelity of the generated maps, we introduce Dice loss and VGG loss into the loss function. Experimental results demonstrate that the proposed method can quickly and accurately generate high‐quality map tiles, outperforming state‐of‐the‐art methods in terms of RMSE, SSIM, PSNR, and Pixel Accuracy metrics.