Standout Papers

Status, advancements and prospects of deep learning methods applied in forest studies 2024 202644
  1. Status, advancements and prospects of deep learning methods applied in forest studies (2024)
    Ting Yun, Ji Zhou et al. International Journal of Applied Earth Observation and Geoinformation

Immediate Impact

55 standout
Sub-graph 1 of 21

Citing Papers

Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation
2024 Standout
Remote Sensing Object Detection in the Deep Learning Era—A Review
2024 Standout
3 intermediate papers

Works of Ting Yun being referenced

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning
2019
A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR
2016
and 1 more

Author Peers

Author Last Decade Papers Cites
Ting Yun 690 367 322 56 965
Hongcan Guan 512 324 189 30 823
Javier Estornell 612 402 321 46 989
Pinliang Dong 582 323 208 60 987
Yuchu Qin 546 501 285 30 1.1k
Anssi Krooks 837 438 210 22 1.0k
Topi Tanhuanpää 850 539 414 29 1.2k
Grant D. Pearse 705 603 224 29 1.0k
Xin Shen 753 520 296 37 947
Jonathan P. Dash 674 560 280 24 997
Guangcai Xu 654 420 372 28 901

All Works

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Rankless by CCL
2026