Feature Importance Analysis of Sentinel-2 Imagery for Large-Scale Urban Local Climate Zone Classification

This paper evaluates different spectral-spatial features that can be extracted from Sentinel-2 imagery regarding their relevance for discriminating different Local Climate Zone (LCZ) classes. The features include spectral reflectance, spectral indices, Morphological Profiles (MPs), as well as Global Urban Footprint (GUF), the Open Street Map layers buildings and land use, and their combinations. Using a residual convolutional neural network (ResNet), a systematic analysis of feature importance is performed with a manually generated dataset distributed in Europe. The results of this evaluation are meant to provide guidance about the choice of both spectral and spatial features for the task of LCZ classification on a global scale. The results show that GUF and OSM can contribute to the classification performance, and ResNet relies less on additional features with the highest accuracy provided by the reflectance only.