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

Qiu, Chunping and Liebel, Lukas and Hughes, Lloyd H. and Schmitt, Michael and Körner, Marco and Zhu, Xiao Xiang (2022) Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification. IEEE Geoscience and Remote Sensing Letters, 19, p. 1000705. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2020.3037246. ISSN 1545-598X.

[img] PDF - Only accessible within DLR bis February 2023 - Published version

Official URL: https://ieeexplore.ieee.org/abstract/document/9269372


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 multitask learning (MTL) is introduced to HSE regression and LCZ classification for the first time. We propose an 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.

Item URL in elib:https://elib.dlr.de/138487/
Document Type:Article
Title:Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Liebel, Lukaslukas.liebel (at) tum.deUNSPECIFIED
Hughes, Lloyd H.lloyd.hughes (at) tum.dehttps://orcid.org/0000-0003-0293-4491
Körner, Marcomarco.koerner (at) tum.dehttps://orcid.org/0000-0002-9186-4175
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:January 2022
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2020.3037246
Page Range:p. 1000705
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:human settlement mapping, remote sensing, climate zone classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Bratasanu, Ion-Dragos
Deposited On:26 Nov 2020 17:39
Last Modified:14 Jan 2022 15:26

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