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

Wurm, Michael, Stark, Thomas, Zhu, Xiao Xiang, Weigand, Matthias, Taubenböck, Hannes·2019·ISPRS Journal of Photogrammetry and Remote Sensing
DOI 10.1016/j.isprsjprs.2019.02.006
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Abstract

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 se…

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  • Semantic segmentation models can predict dense classification maps for arbitrary-sized images in an end-to-end, pixels-to-pixels manner.

    “In contrast, semantic segmentation models can predict dense classification maps for arbitrary-sized images in an end-to-end, pixels-to-pixels manner (Wurm et al., 2019, Chen et al., 2020, Mou et al., 2019).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Wurm et al., 2019

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Metadata

Authors
Wurm, M., Stark, T., Zhu, X. X., Weigand, M., & Taubenböck, H.
ORCID
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2019-04
Volume
150
Pages
59-69
ISSN
0924-2716
DOI
10.1016/j.isprsjprs.2019.02.006
Cited by
332
References
5
Language
en
Licence
Funders
European Research Council (ERC-2016-StG-714087)

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