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Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

Chen, Tzu-Hsin Karen, Qiu, Chunping, Schmitt, Michael, Zhu, Xiao Xiang, Sabel, Clive E., Prishchepov, Alexander V.·2020·Remote Sensing of Environment
DOI 10.1016/j.rse.2020.112096
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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 Chen et al., 2020

    The Introduction says semantic segmentation models provide dense labels within an image patch, and §3.4.2 describes a pixel-labeling architecture whose output has the same dimensions as its input. The paper does not establish that the models accept arbitrary-sized images or explicitly describe prediction as end-to-end; §3.4.3 instead discusses testing specific patch sizes. Page numbers are unavailable in our copy. (LLM, conf=0.82)

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Metadata

Authors
Chen, T.-H. K., Qiu, C., Schmitt, M., Zhu, X. X., Sabel, C. E., & Prishchepov, A. V.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2020-12
Volume
251
Pages
112096
ISSN
0034-4257
DOI
10.1016/j.rse.2020.112096
Cited by
104
References
5
Language
en
Licence
Funders
  • Ministry of Education
  • European Research Council
  • Novo Nordisk
  • China Scholarship Council

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