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Satellite-Based Mapping of Urban Poverty With Transfer-Learned Slum Morphologies

Stark, Thomas and Wurm, Michael and Zhu, Xiao Xiang and Taubenböck, Hannes (2020) Satellite-Based Mapping of Urban Poverty With Transfer-Learned Slum Morphologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (13), 5251 -5263. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.3018862. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/abstract/document/9174807

Abstract

In the course of global urbanization, poverty in cities has been observed to increase, especially in the Global South. Poverty is one of the major challenges for our society in the upcoming decades, making it one of the most important issues in the Sustainable Development Goals defined by the United Nations. Satellite-based mapping can provide valuable information about slums where insights about the location and size are still missing. Large-scale slum mapping remains a challenge, fuzzy feature spaces between formal and informal settlements, significant imbalance of slum occurrences opposed to formal settlements, and various categories of multiple morphological slum features. We propose a transfer learned fully convolutional Xception network (XFCN), which is able to differentiate between formal built-up structures and the various categories of slums in high-resolution satellite data. The XFCN is trained on a large sample of globally distributed slums, located in cities of Cape Town, Caracas, Delhi, Lagos, Medellin, Mumbai, Nairobi, Rio de Janeiro, São Paulo, and Shenzhen. Slums in these cities are greatly heterogeneous inits morphological feature space and differ to a varying degree to formal settlements. Transfer learning can help to improve segmentation results when learning on a variety of slum morphologies, with high F1 scores of up to 89%.

Item URL in elib:https://elib.dlr.de/137087/
Document Type:Article
Title:Satellite-Based Mapping of Urban Poverty With Transfer-Learned Slum Morphologies
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Stark, ThomasThomas.Stark (at) dlr.dehttps://orcid.org/0000-0002-6166-7541
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Date:24 August 2020
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/JSTARS.2020.3018862
Page Range:5251 -5263
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:Paving the Way for the Future of Urban Remote Sensing
ISSN:1939-1404
Status:Published
Keywords:Fully convolutional network (FCN), remote sensing, slum mapping, transfer learning, urban poverty, Xception
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Remote Sensing Technology Institute > EO Data Science
Deposited By: Stark, Thomas
Deposited On:09 Nov 2020 15:36
Last Modified:07 Dec 2020 10:36

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