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

TL;DR

This paper introduces a multi-task learning framework using CNNs to simultaneously perform human settlement extent regression and local climate zone classification, leveraging shared features for improved global urban mapping.

Contribution

First application of multi-task learning to jointly model HSE and LCZ from remote sensing data, enhancing efficiency and accuracy in urban environment mapping.

Findings

The framework achieves competitive accuracy on global datasets.

HSE predictions improve LCZ classification performance.

Shared features reduce redundancy and computational costs.

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.