CartoSR: An attention-enhanced deep GANs for single map super resolution reconstruction

• Optimizing map resolution is crucial for better visual perception and extracting valuable geographical information. • Requiring only the original low-resolution map to accurately enhance its spatial resolution. • Combining generative adversarial networks (GANs) with convolutional neural networks (CNNs) leverages the strengths of both for map reconstruction. • Better dealing with map content than state-of-the-art approaches regarding single image super-resolution. Maps are fundamental media for people to comprehend the spatial and temporal dimensions of a place. Currently, the majority of maps are generated through volunteered efforts, which significantly extends the critical and participatory aspects of geospatial information and provide diverse perspectives and scopes in terms of the geospatial characteristics of a place. However, most of these crowdsourced volunteered maps are often limited in spatial resolution due to file size, editing errors, printing limitations, and image quality issues, among others. This challenge poses a demand for deep learning-enhanced super-resolution reconstruction that can effectively convert low-resolution maps into high-resolution ones. Our previous research has revealed that map reconstruction presents unique requirements for preserving global content and local details involving map annotations and elements, which differ from the objectives of general image reconstruction. In this paper, we integrate MapSR—a CNN-based single map super-resolution reconstruction method we previously developed—into a GAN framework comprising a generator module, a discriminator module, and a local discriminant learning module. This integrated framework enables reconstructing both global map content and individual map elements. To testify the performance of CartoSR, we design three sets of experiments involving comparison with state-of-the-art methods for map super-resolution reconstruction, assessment of GANs’ effectiveness in map reconstruction, and analysis of CartoSR’s scalability. Experimental results demonstrate that our proposed CartoSR achieves state-of-the-art performance in single map super-resolution reconstruction. We hope it can serve as a routine for future research in this area.