Super-resolution GANs for upscaling unplanned urban settlements from remote sensing satellite imagery – the case of Chinese urban village detection

Abstract

The semantic segmentation of informal urban settlements represents an essential contribution towards renovation strategies and reconstruction plans. In this context, however, a big challenge remains unsolved when dealing with incomplete data acquisitions from multiple sensing devices, especially when study areas are depicted by images of different resolutions. In practice, traditional methodologies are directed to downgrade the higher-resolution data to the lowest-resolution measure, to define an overall homogeneous dataset, which is however ineffective in downstream segmentation activities of such crowded unplanned urban environments. To this purpose, we hereby tackle the problem in the opposite direction, namely upscaling the lower-resolution data to the highest-resolution measure, contributing to assess the use of cutting-edge super-resolution generative adversarial network (SR-GAN) architectures. The experimental novelty targets the particular case involving the automatic detection of ‘urban villages’, sign of the quick transformation of Chinese urban environments. By aligning image resolutions from two different data sources (Gaofen-2 and Sentinel-2 data), we evaluated the degree of improvement with regard to pixel-based landcover segmentation, achieving, on a 1 m resolution target, classification accuracies up to 83%, 67% and 56% for 4x, 8x, and 10x resolution upgrades respectively, disclosing the advantages of artificially-upscaled images for segmenting detailed characteristics of informal settlements.

Keywords

• sr-gan
• resolution upscaling
• informal settlements
• urban villages
• cross-sensor analytics