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Shichao Jin(金时超)
Shichao Jin(金时超)
Nanjing Agriculture University
Bestätigte E-Mail-Adresse bei mails.ucas.ac.cn - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Deep learning: individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms
S Jin, Y Su, S Gao, F Wu, T Hu, J Liu, W Li, D Wang, S Chen, Y Jiang, ...
Frontiers in plant science 9, 365925, 2018
1352018
Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data
S Jin, Y Su, F Wu, S Pang, S Gao, T Hu, J Liu, Q Guo
IEEE Transactions on Geoscience and Remote Sensing 57 (3), 1336-1346, 2019
1192019
Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects
S Jin, X Sun, F Wu, Y Su, Y Li, S Song, K Xu, Q Ma, F Baret, D Jiang, ...
ISPRS Journal of Photogrammetry and Remote Sensing 171, 202-223, 2021
1072021
Evaluating maize phenotype dynamics under drought stress using terrestrial lidar
Y Su, F Wu, Z Ao, S Jin, F Qin, B Liu, S Pang, L Liu, Q Guo
Plant methods 15, 1-16, 2019
1042019
An updated vegetation map of China (1: 1000000)
Y Su, Q Guo, T Hu, H Guan, S Jin, S An, X Chen, K Guo, Z Hao, Y Hu, ...
Science Bulletin 65 (13), 1125-1136, 2020
882020
Lidar boosts 3D ecological observations and modelings: A review and perspective
Q Guo, Y Su, T Hu, H Guan, S Jin, J Zhang, X Zhao, K Xu, D Wei, M Kelly, ...
IEEE Geoscience and Remote Sensing Magazine 9 (1), 232-257, 2020
872020
Application of deep learning in ecological resource research: Theories, methods, and challenges
Q Guo, S Jin, M Li, Q Yang, K Xu, Y Ju, J Zhang, J Xuan, J Liu, Y Su, Q Xu, ...
Science China Earth Sciences 63, 1457-1474, 2020
802020
Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data
K Xu, Y Su, J Liu, T Hu, S Jin, Q Ma, Q Zhai, R Wang, J Zhang, Y Li, H Liu, ...
Ecological Indicators 108, 105747, 2020
762020
Separating the structural components of maize for field phenotyping using terrestrial LiDAR data and deep convolutional neural networks
S Jin, Y Su, S Gao, F Wu, Q Ma, K Xu, T Hu, J Liu, S Pang, H Guan, ...
IEEE Transactions on Geoscience and Remote Sensing 58 (4), 2644-2658, 2020
662020
PlantNet: A dual-function point cloud segmentation network for multiple plant species
D Li, G Shi, J Li, Y Chen, S Zhang, S Xiang, S Jin
ISPRS Journal of Photogrammetry and Remote Sensing 184, 243-263, 2022
522022
Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
S Jin, Y Su, S Song, K Xu, T Hu, Q Yang, F Wu, G Xu, Q Ma, H Guan, ...
Plant Methods 16, 1-19, 2020
462020
A global corrected SRTM DEM product for vegetated areas
X Zhao, Y Su, T Hu, L Chen, S Gao, R Wang, S Jin, Q Guo
Remote Sensing Letters 9 (4), 393-402, 2018
452018
The influence of vegetation characteristics on individual tree segmentation methods with airborne LiDAR data
Q Yang, Y Su, S Jin, M Kelly, T Hu, Q Ma, Y Li, S Song, J Zhang, G Xu, ...
Remote Sensing 11 (23), 2880, 2019
432019
The development and evaluation of a backpack LiDAR system for accurate and efficient forest inventory
Y Su, Q Guo, S Jin, H Guan, X Sun, Q Ma, T Hu, R Wang, Y Li
IEEE Geoscience and Remote Sensing Letters 18 (9), 1660-1664, 2020
392020
Loess landslide detection using object detection algorithms in northwest China
Y Ju, Q Xu, S Jin, W Li, Y Su, X Dong, Q Guo
Remote Sensing 14 (5), 1182, 2022
372022
Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives
H Tao, S Xu, Y Tian, Z Li, Y Ge, J Zhang, Y Wang, G Zhou, X Deng, ...
Plant Communications 3 (6), 2022
352022
A novel framework to automatically fuse multiplatform LiDAR data in forest environments based on tree locations
H Guan, Y Su, T Hu, R Wang, Q Ma, Q Yang, X Sun, Y Li, S Jin, J Zhang, ...
IEEE Transactions on Geoscience and Remote Sensing 58 (3), 2165-2177, 2019
352019
A point-based fully convolutional neural network for airborne lidar ground point filtering in forested environments
S Jin, Y Su, X Zhao, T Hu, Q Guo
IEEE journal of selected topics in applied earth observations and remote …, 2020
342020
The transferability of Random Forest in canopy height estimation from multi-source remote sensing data
S Jin, Y Su, S Gao, T Hu, J Liu, Q Guo
Remote Sensing 10 (8), 1183, 2018
342018
Automatic object detection of loess landslide based on deep learning
Y Ju, Q Xu, S Jin, W Li, X DONG, Q GUO
Geomatics and Information Science of Wuhan University 45 (11), 1747-1755, 2020
332020
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