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arXiv · 2011.11452

Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification

Chunping Qiu·Lukas Liebel·Lloyd H. Hughes·Michael Schmitt·Marco Körner·Xiao Xiang Zhu
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Abstract

Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e.g., for sustainable urban development and Urban Heat Island (UHI) studies. Remote sensing (RS)- and deep learning (DL)-based classification approaches play a significant role by providing the potential for global mapping. However, most of the efforts only focus on one of the two schemes, usually on a specific scale. This leads to unnecessary redundancies, since the learned features could be leveraged for both of these related tasks. In this letter, the concept of multi-task learning (MTL) is introduced to HSE regression and LCZ classification for the first time. We propose a MTL framework and develop an end-to-end Convolutional Neural Network (CNN), which consists of a backbone network for shared feature learning, attention modules for task-specific feature learning, and a weighting strategy for balancing the two tasks. We additionally propose to exploit HSE predictions as a prior for LCZ classification to enhance the accuracy. The MTL approach was extensively tested with Sentinel-2 data of 13 cities across the world. The results demonstrate that the framework is able to provide a competitive solution for both tasks.

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Chunping Qiu, Lukas Liebel, Lloyd H. Hughes, Michael Schmitt, Marco Körner, Xiao Xiang Zhu. 2020-11-23. Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification. https://arxiv.org/abs/2011.11452

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