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Using different training strategies for urban land-use classification based on convolutional neural networks

Overview of attention for article published in Frontiers in Environmental Science, August 2022
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Mentioned by

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1 X user

Citations

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3 Dimensions

Readers on

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9 Mendeley
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Title
Using different training strategies for urban land-use classification based on convolutional neural networks
Published in
Frontiers in Environmental Science, August 2022
DOI 10.3389/fenvs.2022.981486
Authors

Tianqi Qiu, Huagui He, Xiaojin Liang, Fei Chen, Zhaoxia Chen, Yang Liu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 11%
Lecturer 1 11%
Other 1 11%
Student > Postgraduate 1 11%
Unknown 5 56%
Readers by discipline Count As %
Arts and Humanities 1 11%
Engineering 1 11%
Unknown 7 78%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 September 2022.
All research outputs
#20,812,371
of 23,419,482 outputs
Outputs from Frontiers in Environmental Science
#2,050
of 3,723 outputs
Outputs of similar age
#344,787
of 432,363 outputs
Outputs of similar age from Frontiers in Environmental Science
#240
of 449 outputs
Altmetric has tracked 23,419,482 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,723 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 432,363 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 449 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.