Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
This work provides a more efficient method for generating two crucial urban planning and climate study maps, reducing redundancy for researchers and practitioners in urban development and environmental science.
This paper addresses the problem of simultaneously mapping Human Settlement Extent (HSE) and Local Climate Zones (LCZ) using remote sensing data. The proposed multi-task learning framework achieves competitive performance for both tasks across 13 global cities.
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.