The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data
[Download]Jin et al., 2018. Remote Sensing
Welcome to my personal website!
I'm now a Ph.D candidate major in deep learning and remote sensing in University of Chinese Academy of Sciences...
Educations
Ph.D. in Ecology, University of Chinese Academy of Sciences, Beijing, China 2016-2020expected
Dissertation title: Maize phenotypic trait extraction and biomass estimation using deep learning from terrestrial lidar data.
B.S. in in Forestry, Huazhong Agriculture University, Wuhan, China 2012- 2016
Dissertation title: Three-dimensional Structure Parameters Extraction and Geographical Distribution of Quercus Mongolica Based on Lidar Technology
Honors & Awards
1st Prize Winner (1/800) of the Second International Student Academic Forum, Institute of Botany, Chinese Academy of Sciences 2018
Best popularity award of the Second International Student Academic Forum, Institute of Botany, Chinese Academy of Sciences. 2018
Merit Student, University of Chinese Academy of Sciences 2017, 2019
Outstanding Graduates, Huazhong Agriculture University 2016
National Top Ten Graduates of Forestry (The undergraduate group), Chinese Society of Forestry & National Forestry Administration 2016
The Liang Xi Outstanding Student Award (< 1/1000), National Forestry Administration 2016
Scholarship of the University of Chinese Academy of Sciences 2014, 2015
Top 10 outstanding undergraduate student (10/20000) in Huazhong Agriculture University2016
National Scholarship (1/500), Ministry of Education of the People's Republic of China 2015
National Scholarship (1/500), Ministry of Education of the People's Republic of China 2014
National Scholarship (1/500), Ministry of Education of the People's Republic of China 2013
1st prize (< 1/1000) of college students' biological experiment skill competition in Hubei province, Department of Education, Hubei Province 2014
Merit Student, Huazhong Agriculture University 2013, 2014, 2015
Featured Works
Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms
[Download]Jin et al., 2018, Frontiers in Plant Science
Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data
Jin et al., 2018, IEEE Transactions on Geoscience and Remote Sensing
[Download]
Separating the structural components of maize for field phenotyping using terrestrial lidar data and deep convolutional neural networks
Jin et al., 2019.IEEE Transactions on Geoscience and Remote Sensing
Evaluating maize phenotype dynamics under drought stress using terrestrial lidar
Su, Y., Wu, F., Ao, Z., Jin, S., Qin, F., Liu, B., Pang, S., Liu, L., & Guo, Q. (2019). Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant methods, 15, 11
A global corrected SRTM DEM product for vegetated areas
Zhao, X., Su, Y., Hu, T., Chen, L., Gao, S., Wang, R., Jin, S., & Guo, Q. (2018). A global corrected SRTM DEM product for vegetated areas. Remote Sensing Letters, 9, 393-402
All publications
Last update on 2019/12/6
Manuscripts under Review or in Final Preparation
Jin, S., et al., 2019. A Point-Based Fully Convolutional Neural Network for Airborne Lidar Ground Point Filtering in Forested Environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Minor revision).
Jin, S., Su, Y., Guo, Q. et al., 2019. Estimating maize biomass from group level to individual level using lidar data. (Preparing for Plant Methods)
Su, Y., Guo, Q., Jin, S., et al. 2019. Backpack LiDAR: an efficient and accurate tool for forest inventory. Forest Ecology and Management. (Under review)
Su, Y., Guo, Q., Hu T., Guan, H., Jin, S., et al. 2019. An updated Vegetation Map of China (1:1000000) (Science Bulletin, minor revision)
Su, Y., Hu T., Wang Y., Li Y., Dai J., Liu H., Jin, S., et al. 2019. Large Scale Geological and Climatic Factors Control Tree Crown Shapes. Journal of Geophysical Research. (Under review)
2019
Jin, S., et al., 2019. Separating the structural components of maize for field phenotyping using terrestrial lidar data and deep convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing (Accepted).
Yang, Q.#, Su, Y. #, Jin, S.#, et al. 2019. The influences of vegetation characteristics on individual tree segmentation methods with airborne LiDAR data. Remote sensing. Remote sensing.11(23):1-18.
Guo, Q. and Jin, S. et al., Deep learning Review. Science China Earth Sciences (Minor revision, In Chinese).
Zheng Z, Ma Q, Jin, S., et al. 2019. Canopy and terrain interactions affecting snowpack spatial patterns in the Sierra Nevada of California[J]. Water Resources Research. (Accepted)
Xu, K., Su, Y., Liu, J., Hu, T., Jin, S., Ma, Q., Zhai, Q., Wang, R., Zhang, J., Li, Y., Liu, H., Guo, Q., 2019. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data. Ecological Indicators 108, 105747.
Su, Y., Wu, F., Ao, Z., Jin, S., Qin, F., Liu, B., Pang, S., Liu, L., Guo, Q*. 2019. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods.15:11.
Guan, H., Su, Y.*, Hu, T., Wang, R., Ma, Q., Yang, Q., Sun, X., Li, Y.,Jin, S., Zhang, J., Ma, Q., Liu, M., Wu, F. Guo, Q. 2019. A Novel Framework to Automatically Fuse Multi-platform Lidar Data in Forest Environments Based on Tree Locations. IEEE Transactions on Geoscience and Remote Sensing. (Accepted).
2018
Guo, Q.*, Yang, W., Wu, F., Pang, S., Jin, S., Chen, F., Wang, X.. 2018. High-throughput Crop Phenotyping: Accelerators for Development of Breeding and Precision Agriculture[J].Bulletin of the Chinese Academy of Sciences. 33(9):940-946.(In Chinese).
Guo, Q.*, Hu, T., Jiang, Y., Jin, S., Wang, R., Guan, H., Yang, Q., Li, Y., Wu, F., Zhai, Q., Liu, J., Su, Y.. 2018. Advances in remote sensing application for biodiversity research. Biodiversity Science. 26(8):789-806.(In Chinese).
Jin, S.#, Su, Y.#, Gao, S., Hu, T., Liu, J., Guo, Q*. 2018. The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data. Remote Sensing, 10(8): 1183-1203.
Jin, S., Su, Y., Wu, F., Pang, S., Gao, S., Hu, T., Liu, J., Guo, Q*. 2018. Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data. IEEE Transactions on Geoscience and Remote Sensing. 57(3): 1336-1346.
Jin, S., Su, Y.*, Gao, S., Wu, F., Hu, T., Liu, J., Li, W., Wang, D., Chen, S., Jiang, Y., Pang, S., Guo, Q*. 2018. Deep Learning: Individual maize segmentation from terrestrial Lidar data using Faster R-CNN and regional growth algorithms. Frontiers in Plant Science, 9: 866-875.
Zhao, X.#, Su, Y.#, Hu, T., Chen, L., Gao, S., Wang, R., Jin, S., Guo, Q.*, 2018. A global corrected SRTM DEM product over vegetated areas. Remote Sensing Letters. 9(4): 393-402.
Patents
Jin, S., Dian Y., Wang P., Teng M., Meng F., Peng L. 2018. A method and system for canopy cover calculation based on digital images. No: CN 105719320 B
Invited Talks and Posters
Jin, S., Su Y., Guo Q. 2019. A Deep Learning Pipeline for High-Throughput Maize Phenotyping from Lidar Data, International Student Research Forum 2019, Denmark. (Oral, selected)
Jin, S., Su Y., Guo Q. Vegetation mapping using crowdsourcing methods. Chinese Research Academy of Environmental Sciences. Beijing, China. 2019. (Oral, invited)
Jin, S., Su Y., Hu T., Guo Q. A deep learning pipeline for high throughput crop phenotyping. AGU. Washington, D.C., USA. 2018 Fall. (Abstract)
Jin, S., Su Y., Wu F., Guo Q. A deep learning pipeline for high throughput crop phenotyping. The second Asia-Pacific Plant Phenotyping Conference. Nanjing, CHINA. 2018. (Oral)
Zheng Z., Ma Q., Jin, S., Su Y., Guo Q., Bales R, "Canopy and terrain interactions on spatial distributions of snowpack in the Sierra Nevada" oral presentation in AGU, Washington DC. 2018 Fall.
Working experiences
Workshop lecturer, Institute of Botany, Chinese Academy of Sciences, 2019 summer, "LiDAR applications in Forestry Ecology"
Workshop lecturer, Institute of Botany, Chinese Academy of Sciences, 2018 summer, "LiDAR applications in Forestry Ecology"
Skills
Familiar with most remote sensing data processing, especially the lidar data.
Familiar with mainstream programming language (e.g., Python, R, Matlab) and software (e.g., ArcGIS, ENVI, eCognition, QGIS).
Familiar with mainstream deep learning methods (e.g., CNN, GNN, RNN) and toolkits (e.g., PyTorch, TensorFlow, Keras).
Intermediate level of writing and communication skills.
Projects
The Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA08040107), "Research and development of nondestructive phenotypic system", Crop 3D hardware testing and software algorithm development (Jin et al., 2018, IEEE TGRS; Jin et al., 2018, Frontiers in Plant Science; Jin et al., 2019, IEEE TGRS)
The Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19050401), "Research and development of nondestructive phenotypic system", Design the crowdsourcing mobile application "Li-vegetation". National Key R&D Program of China (Grant Nos. 2017YFC0503905 and 2016YFC0500202), and National Science Foundation of China (Grant No. 41471363, 31741016). Remote sensing data collection and analysis (Jin et al., 2018, Remote Sensing).
Get In Touch
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