Effective Classification of Local Climate Zones Based on Multi-Source Remote Sensing Data

The local climate zone (LCZ) classification divides the urban areas into 17 categories, which are composed of 10 manmade structures and 7 natural landscapes. Though originally designed for temperature study, LCZ classification can be used for studies on economy and population. In this paper, we achieve a LCZ classification with convolutional neural networks based on the multi-source remote sensing data, including the polarimetric synthetic aperture radar (PolSAR) data and the corresponding multi-spectral imagery (MSI). Through experiments we attempt to reveal the contributions of the SAR data and the MSI to the classification performance. Furthermore, we emphasize the crucial importance of the preprocessing on the training data to derive a balanced dataset. We are ranked second in the Tianchi competition rankings when we submit our results.

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