| 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 | 143 | 2018 |
| 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 | 129 | 2019 |
| 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 | 125 | 2021 |
| 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 | 115 | 2019 |
| 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 | 92 | 2020 |
| 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 | 90 | 2020 |
| 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 | 80 | 2020 |
| 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 | 80 | 2020 |
| 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 | 73 | 2020 |
| 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 | 68 | 2022 |
| 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 | 52 | 2020 |
| 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 | 47 | 2019 |
| 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 | 46 | 2018 |
| 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 | 44 | 2022 |
| 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 | 44 | 2020 |
| 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 | 43 | 2022 |
| 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 | 41 | 2019 |
| A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images Y Cheng, S Lan, X Fan, T Tjahjadi, S Jin, L Cao International Journal of Applied Earth Observation and Geoinformation 124 …, 2023 | 40 | 2023 |
| Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing Z Sun, Q Li, S Jin, Y Song, S Xu, X Wang, J Cai, Q Zhou, Y Ge, R Zhang, ... Plant Phenomics, 2022 | 37 | 2022 |
| 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 | 37 | 2020 |
Qinghua GuoPeking University在 pku.edu.cn 的电子邮件经过验证
