Learning Self-Adaptive Scales for Extracting Urban Functional Zones From Very-High-Resolution Satellite Images

Abstract

Urban functional zones (e.g. commercial, residential, and industrial) are basic units for city planning and management, and play an important role in city studies. However, functional zones are difficult to extract from very-high-resolution (VHR) remote sensing images, as they are various in components, sizes, and heterogeneities, leading to different segmentation scales. To resolve this issue, this study uses selfhood scale, a local optimum scale, to extract functional zones. Firstly, geoscene segmentation is used to delineate functional zones at multiple scales. Then, selfhood scales are calculated to measure the local optimum scales of segmenting functional zones, based on which multiscale segmentation results can be finally assembled into one layer to generate functional-zone boundaries. The experimental results indicate this method that is effective to delineate functional zones in Beijing, adapting to local built environments.