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Towards Global Slum Mapping From Space: Detecting Urban Poverty Using a Transfer Learned Fully Convolutional Network

Stark, Thomas and Wurm, Michael and Taubenböck, Hannes and Zhu, Xiao Xiang (2019) Towards Global Slum Mapping From Space: Detecting Urban Poverty Using a Transfer Learned Fully Convolutional Network. Phi-week 2019, 09.-13. Sep. 2019, Rom, Italien.

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Informal settlements are the result of the continuing rapid growth of mega cities, especially in the global south where people migrate from rural areas in hope for a better future. Poverty is considered one of the major challenges to our society in the upcoming decades, making it one of the most important issues in the Sustainable Developments Goals as defined by the United Nations. These settlements, however, often lack basic sanitation and access to clean water. While many urban agglomerations of the global south are prone to large slum areas, still, the exact location and size of these settlements is often unknown. Remote sensing methods have improved tremendously in their capabilities of mapping informal settlements and its morphological features, which can be described by their high building density, small building sizes or its building orientation. But the challenge of large scale slum mapping still remains open, due to fuzzy feature spaces between formal and informal settlements, as well as a significant imbalance of slum occurrences where slums only account for 1% in the data. To tackle this issue we propose a fully convolutional xception network (XFCN). With its 34 convolutional and five dilated convolutional layers including four skip connections during the up-sampling phase the XFCN is capable of detecting poor urban areas in a coherent transfer learning approach using high resolution satellite images. This proves to be an ambitious task, differentiating between formal built-up structures and informal settlements at high resolutions. We train the network on a large sample of globally distributed slums (Cape Town, Caracas, Delhi, Dhaka, Lagos, Medellin, Mumbai, Nairobi, Rio de Janeiro, São Paulo and Shenzhen), greatly heterogeneous in its morphologic feature space and transfer the XFCN to map informal settlements. The XFCN is trained from scratch using 5 input channels and rigorous regularization. Using this approach we are able to reach an overall accuracy of up to 95%.

Item URL in elib:https://elib.dlr.de/129502/
Document Type:Conference or Workshop Item (Speech)
Title:Towards Global Slum Mapping From Space: Detecting Urban Poverty Using a Transfer Learned Fully Convolutional Network
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
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:11 September 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Deep learning, fully convolutional network, transfer learning, remote sensing, urban poverty
Event Title:Phi-week 2019
Event Location:Rom, Italien
Event Type:international Conference
Event Dates:09.-13. Sep. 2019
Organizer:Europen Space Agency
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:18 Oct 2019 11:59
Last Modified:18 Oct 2019 11:59

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