DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Slum Mapping in Imbalanced Remote Sensing Datasets Using Transfer Learned Deep Features

Stark, Thomas and Wurm, Michael and Taubenböck, Hannes and Zhu, Xiao Xiang (2019) Slum Mapping in Imbalanced Remote Sensing Datasets Using Transfer Learned Deep Features. IEEE. JURSE 2019, 22.-24. May 2019, Vannes, Frankreich. DOI: 10.1109/jurse.2019.8808965

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/8808965


Unprecedented urbanization, particularly in countries of the Global South, results in the formation of slums. Here, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. Recent advances in transferring deep features learned in fully convolutional networks (FCN) allow the specific structural types and alignments of buildings in slums to be mapped. The class imbalance of slums is especially challenging in the context of intra-urban variability of slums themselves, and their possible similarity to other urban built-up structures. Thus, in our study we aim to analyze the transfer learning capabilities of FCNs for slum mapping with respect to training on imbalanced datasets and the quantity of available training images. When the slum sample proportion is increased an improvement of the Intersection over Union (IU) of 10% to 30% can be observed. Increasing the total number of images improves the IU up to 20% to 50%. Transfer learning proves extremely valuable in retrieving information on complex and heterogeneous urban structures such as slum patches.

Item URL in elib:https://elib.dlr.de/128983/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Slum Mapping in Imbalanced Remote Sensing Datasets Using Transfer Learned Deep Features
AuthorsInstitution or Email of AuthorsAuthors 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:May 2019
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/jurse.2019.8808965
Page Range:pp. 1-4
Keywords:remote sensing, urban poverty, slums, transfer learning, fully convolutional network, deep learning
Event Title:JURSE 2019
Event Location:Vannes, Frankreich
Event Type:international Conference
Event Dates:22.-24. May 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Stark, Thomas
Deposited On:19 Sep 2019 12:16
Last Modified:06 Dec 2019 17:56

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.