Multilevel ensembling for local climate zones classification

This paper presents an end-to-end system for automatic local climate zones classification of various types of urban environment. For that we perform fusion of multispectral images from Landsat-8 and Sentinel-2 satellites with site description extracted from OpenStreetMap layers. The proposed classification approach is based on a multi-level ensemble scheme that combines Convolutional Neural Networks, Random Forests and Gradient Boosting Machines.