Multi-Label Local Climate Zone Mapping as Scene Classification Using Very High Resolution Imagery: Preliminary Result of Hong Kong

Abstract

With the near completion of WUDAPT (World Urban Database and Access Portal Tools) Level 0 data, one of the next goals is to generate more accurate and detailed local climate zone (LCZ) maps. An important issue is how to integrate building height information into LCZ maps. We here present a multi-label classification method using very high resolution (VHR) imagery to implicitly integrate building height information. Since we humans can tell whether a place is high-rise or not based on the shading of buildings and the surrounding context, it is possible to extract such information using deep learning methods. We use Hong Kong as a case study and show the potential of LCZ mapping with VHR imagery in distinguishing small-scale landscape features like city parks. The multi-label LCZ maps also provide a solution to generate fine-grained subclass LCZ mapping, in which a place can be classified as a combination of multiple LCZs, e.g., compact low-rise with open high-rise.