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Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks

Wurm, Michael and Stark, Thomas and Zhu, Xiao Xiang and Weigand, Matthias and Taubenböck, Hannes (2019) Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 150, pp. 59-69. Elsevier. DOI: 10.1016/j.isprsjprs.2019.02.006 ISSN 0924-2716

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Official URL: https://www.sciencedirect.com/science/article/pii/S0924271619300383


Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: QuickBird image obtains 86-88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.

Item URL in elib:https://elib.dlr.de/126606/
Document Type:Article
Title:Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Stark, ThomasThomas.Stark (at) dlr.deUNSPECIFIED
Weigand, Matthiasmatthias.weigand (at) dlr.dehttps://orcid.org/0000-0002-5553-4152
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Date:April 2019
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.isprsjprs.2019.02.006
Page Range:pp. 59-69
Keywords:Slums; FCN; Convolutional neural networks; Deep learning; Transfer learning
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:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Wurm, Michael
Deposited On:11 Mar 2019 12:36
Last Modified:06 Sep 2019 15:28

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