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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Srivastava, Shivangi, Vargas-Muñoz, John E., Tuia, Devis·2019·Remote Sensing of Environment
DOI 10.1016/j.rse.2019.04.014
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  • There are two general strategies for deep-learning-based land cover classification: object-oriented approaches based on DCNNs and semantic segmentation approaches based on end-to-end DCNNs.

    “There are two general strategies for deep-learning-based land cover classification: object-oriented approaches based on DCNNs and semantic segmentation approaches based on end-to-end DCNNs.”

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

    The Introduction and Related Work distinguishes CNN-based patch classification from classifying all pixels in one forward pass, and the Methods contrasts urban-object classification with pixel-level semantic segmentation. It does not frame these as the two general strategies for deep-learning-based land-cover classification, or specify the claimed object-oriented DCNN versus end-to-end DCNN division. Page numbers are not available in our copy. (LLM, conf=0.82)

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Research works printed but not linked: 34

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Metadata

Authors
Srivastava, S., Vargas-Muñoz, J. E., & Tuia, D.
ORCID
Tuia, Devis
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2019-07
Volume
228
Pages
129-143
ISSN
0034-4257
DOI
10.1016/j.rse.2019.04.014
Cited by
151
References
4
Language
en
Licence
Funders
Swiss National Science Foundation (PZ00P2-136827)

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