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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 (#45 of 12,013)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
145 tweeters
peer_reviews
1 peer review site

Readers on

mendeley
25 Mendeley
Summary Twitter Peer reviews
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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

View on publisher site

Twitter Demographics

The data shown below were collected from the profiles of 145 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 6 24%
Student > Ph. D. Student 6 24%
Student > Master 4 16%
Student > Bachelor 2 8%
Student > Doctoral Student 2 8%
Other 1 4%
Unknown 4 16%
Readers by discipline Count As %
Unspecified 6 24%
Environmental Science 6 24%
Agricultural and Biological Sciences 2 8%
Engineering 2 8%
Earth and Planetary Sciences 1 4%
Other 2 8%
Unknown 6 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 102. 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 13 April 2023.
All research outputs
#374,526
of 23,818,521 outputs
Outputs from Remote Sensing
#45
of 12,013 outputs
Outputs of similar age
#8,513
of 420,397 outputs
Outputs of similar age from Remote Sensing
#2
of 273 outputs
Altmetric has tracked 23,818,521 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,013 research outputs from this source. They receive a mean Attention Score of 4.4. 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 420,397 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 97% of its contemporaries.
We're also able to compare this research output to 273 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.

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