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Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning

Overview of attention for article published in Remote Sensing, March 2023
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#20 of 14,366)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
250 X users
peer_reviews
1 peer review site

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
109 Mendeley
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Title
Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning
Published in
Remote Sensing, March 2023
DOI 10.3390/rs15051463
Authors

Mirela Beloiu, Lucca Heinzmann, Nataliia Rehush, Arthur Gessler, Verena C. Griess

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 250 X users 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 109 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 11%
Student > Master 9 8%
Student > Bachelor 7 6%
Researcher 7 6%
Student > Doctoral Student 4 4%
Other 9 8%
Unknown 61 56%
Readers by discipline Count As %
Environmental Science 21 19%
Earth and Planetary Sciences 6 6%
Agricultural and Biological Sciences 5 5%
Unspecified 3 3%
Engineering 3 3%
Other 9 8%
Unknown 62 57%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 173. 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 01 August 2024.
All research outputs
#252,758
of 26,746,748 outputs
Outputs from Remote Sensing
#20
of 14,366 outputs
Outputs of similar age
#6,284
of 435,746 outputs
Outputs of similar age from Remote Sensing
#2
of 501 outputs
Altmetric has tracked 26,746,748 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,366 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 99% of its peers.
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 435,746 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 501 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.