Qiqi Zhu
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Dr. Zhu is an Associate Professor in the School of Geography and Information Engineering at China University of Geosciences (Wuhan).

Dr. Zhu started her master's degree exempting from the postgraduate entrance examination in Wuhan University, 2013. She then went on to successive postgraduate and doctoral program under the tutorship of Academician Deren Li, Professor Yanfei Zhong, and Professor Liangpei Zhang in 2015, and received her doctoral degree(Photogrammetry and Remote Sensing) in June 2018, from State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing(LIESMARS), Wuhan university, China. In July 2018, Dr. Zhu was introduced to the Department of Geography, School of Geography and Information Engineering, China University of Geosciences (Wuhan) as an Outstanding Young Talent.

      Devoted herself to the research on remote sensing  data extraction, analysis and application, Dr. Zhu has published more than 30 SCI papers. Two SCI papers have received ESI Highly Cited Papers since 2017. She is the PI and CO-PI of more than ten projects, including the National Key Research and Development Programs, National Natural Science Foundation of China, and National Development and Reform Commission Projects.

Dr. Zhu is also serving as the reviewers of International SCI journals, such as IEEE Transactions on Cybernetics、Remote Sensing of Environment、ISPRS Journal of Photogrammetry and Remote Sensing、IEEE Transaction on Geoscience and Remote Sensing、IEEE Transactions on Knowledge and Data Engineering、IEEE Journal of Biomedical and Health Informatics、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing、IEEE Geoscience and Remote Sensing Letter、Remote Sensing、International Journal of Remote Sensing.


Research Interests:

Based on high-resolution,hyperspectral, multi-source remote sensingand geographic data, the research interests of Dr. Zhu focus on:

(1)Probability graph model, transfer learning, deep learning and other machine learning methods

(2)Scene classification, target detection, semantic segmentation, video target tracking, road extraction, change detection and other remote sensing image interpretation tasks

(3)Urban remote sensing, functional area planning, agricultural remote sensing, geographic information services and other applications


For students interested in RS/GIS/computer science and technology/mathematics/surveying and mapping/image processing 

Welcome to join us!

Resource Sharing: 

1.Public Dataset

(1)The CHN6-CUG Road Dataset (update time:2021.5.25).  

Baidu Drive (extraction code: urs6)download

Google Drivedownload

CHN6-CUG Road Dataset was produced and shared by the team of Qiqi Zhu from URSmart research group of China University of Geosciences, Wuhan.

 It is the first new large-scale satellite remote sensing image road data set of representative cities in China.  

This dataset is a pixel level high-resolution satellite image with artificial label and 6 representative cities in China are selected.

CHN6-CUG contains 4511 labeled images of 512×512 size, divided into 3608 for model training and 903 for testing and result evaluation, with a resolution of 50 cm/pixel. 

When this dataset is used in your published results, please refer to the following literature:

      [1]Q. Zhu, Y. Zhang, L. Zeng, Y. Zhong, Q. Guan, L. Zhang, and D. Li, “A Global Context-aware and Batch-independent Network for Road Extraction from VHR Satellite Imagery,”ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, pp. 353–365, 2021.

(2)Deep-SAR Oil Spill (SOS) dataset (update time:2021.9.26).                                                                                                                                            

Baidu Drive (extraction code: urs6)download

 Google Drivedownload

This data set has two study areas, the Gulf of Mexico oil spill area and the Persian Gulf oil spill area.

 It is a pixel-level data set of oil spill and non-oil spill,and is made available for research purposes only.

PALSAR images were used in the Gulf of Mexico study area. Sentinel 1A images were used for the Persian Gulf study area. It is collected and produced by the URSmart research group of China University of Geosciences.  

3101 samples from Mexico's oil spill area were used for model training, and 776 samples were used for testing. 3354 samples from the oil spill area of the Persian Gulf were used for training and 839 samples were used for testing.

(3)The Google image dataset of SIRI-WHU (update time:2019.12.10).  Download

This dataset consists of 12 categories and is mainly used for scientific research purposes.

Each of the following categories contains 200 images: farms, commercial areas, ports, idle land, industrial areas, grasslands, overpasses, parking lots, ponds, residential areas, rivers, water.

Each image has a size of 200*200 and a spatial resolution of 2m.

This dataset was obtained from Google Earth and collected and producted by RS-Idea research group(siri-whu)of wuhan university.

When this dataset is used in your published results, please refer to the following literature:

    [1]Q. Zhu, Y. Zhong, L. Zhang, and D. Li, "Adaptive Deep Sparse Semantic Modeling Framework for High Spatial Resolution Image Scene Classification," IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 6180-6195. DOI: 10.1109/TGRS.2018.2833293.

    [2]Q. Zhu, Y. Zhong, B. Zhao, G.-S. Xia, and L. Zhang, "Bag-of-Visual-Words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery," IEEE Geoscience and Remote Sensing Letters, 2016, 13(6): 747-751. DOI:10.1109/LGRS.2015.2513443 2016.

(4)The USGS image dataset of SIRI-WHU (update time:2019.12.10).  Download

This dataset consists of four scene categories: farms, forests, residential areas and parking lots, which are mainly used for scientific research purposes.

The large image size is 10000*9000,and the spatial resolution is 2 feet.

The dataset was obtained from USGS, collected and produced by RS-IDEA Research Group (Siri-WHU) of Wuhan University, and mainly covered Montgomery, Ohio, USA.

When this dataset is used in your published results, please refer to the following literature:

     [1]Y. Zhong, Q. Zhu, and L. Zhang, "Scene Classification Based on the MultiFeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 6207-6222, Nov. 2015. 

2. Source Code 

(1)The Insufficient and Imbalanced Hyperspectral Image Classification Code and Dataset (update time:2021.06.01).  

Github: https://github.com/CUG-URS/SSDGL

A spectral-spatial dependent global learning (SSDGL) framework based on global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification.  In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.

When this Code is used in your published results, please refer to the following literature:

     [1]Q. Zhu, W. Deng, Z. Zheng, Y. Zhong, Q. Guan, W. Lin, L. Zhang, and D. Li, “A Spectral-Spatial Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification,”IEEE Transactions on Cybernetics, 2021. 

Representative Publications:

  • Q. Zhu, Y. Lei, X. Sun, Q. Guan, Y. Zhong, L. Zhang, and D. Li, “Knowledge-guided land pattern depiction for urban land use mapping: A case study of Chinese cities,”Remote Sensing and Environment, vol. 272, pp.112916, 2022.  (IF=10.164)

  • Q. Zhu, X. Guo, W. Deng, Q. Guan, Y. Zhong, L. Zhang, and D. Li, “Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, 184 (2022): 63-78, 2022.  (IF=8.979)

  • Q. Zhu, W. Deng, Z. Zheng, Y. Zhong, Q. Guan, W. Lin, L. Zhang, and D. Li, “A Spectral-Spatial Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification,” IEEE Transactions on Cybernetics, 2021. (IF=11.448)

  • Q. Zhu, Y. Zhang, L. Wang, Y. Zhong, Q. Guan, L. Zhang, and D. Li, “A Global Context-aware and Batch-independent Network for Road Extraction from VHR Satellite Imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, pp. 353–365, 2021. (IF=8.979)

  • Q. Zhu, Y. Yang, X.Sun, M. Guo, “CDANet: Contextual Detail-Aware Network for High-Spatial-Resolution Remote-Sensing Imagery Shadow Detection,” IEEE Transactions on Geoscience and Remote Sensing, 2022. (IF=5.600)

  • Q. Zhu, Y. Zhang, Z. Li, X. Yan, Q. Guan, Y. Zhong,  L. Zhang, and D. Li, “Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images,” IEEE Transactions on Geoscience and Remote Sensing2021.(IF=5.600)

  • Q. Zhu, L. Wang, J. Chen, W. Zen, Y. Zhong, Q. Guan, Z. Yang, “S3TRM: Spectral-Spatial Unmixing of Hyperspectral Imagery Based on Sparse Topic Relaxation-Clustering Model,” IEEE Transactions on Geoscience and Remote Sensing, 2021. (IF=5.600)

  • Q. Zhu, Y. Zhong, L. Zhang, and D. Li, “Adaptive deep sparse semantic modeling framework for high spatial resolution image scene classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 10, pp. 6180 – 6195, 2018. (IF=5.600)

  • Y. Zhong, Q. Zhu, and L. Zhang, “Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing magery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 6207–6222, Nov. 2015. ( ESI highly cited,  IF=5.600)

  • Q. Zhu, Y. Zhong, S. Wu, L. Zhang, and D. Li, “Scene classification based on the sparse homogeneous-heterogeneous topic feature model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 5,pp. 2689 – 2703, 2018. ( IF=5.600)

  • Q. Zhu, Y. Zhong, L. Zhang, and D. Li, “Scene classification based on the fully sparse semantic topic model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 10, pp. 5525 – 5538, 2017. ( IF=5.600)

  • Q. Zhu, Y. Zhong, B. Zhao, G. S. Xia, and L. Zhang, “Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 6, pp. 747–751, Jun. 2016(ESI highly cited,  IF=3.966)

  • Q. Zhu, Z. Li, Y. Zhang, J. Li, Y. Du, Q. Guan, and D. Li, Global-Local-Aware conditional random fields based building extraction for high spatial resolution remote sensing images,” National Remote Sensing Bulletin, vol.25, no.7, pp.1422-1433, 2021(EI

  Academic activities:

Reviewers of 

Remote Sensing of Environment

IEEE Transaction on Geoscience and Remote Sensing

IEEE Transactions on Cybernetics

IEEE Transactions on Knowledge and Data Engineering

IEEE Journal of Biomedical and Health Informatics

IEEE Journal of Selected Topics in Applied Earth observations and Remote Sensing

IEEE Geoscience and Remote Sensing Letter

Remote Sensing

IEEE Access

International Journal of Remote Sensing

Remote Sensing Letters


      Scientific Research and Fund Project:

   l Project supported by the National Science Foundation for Young Scientists of China ( Grant No.41901306 ):" Research on scene classification method of high-resolution Remote Sensing image based on deep topic model ", Time: 2020.01 -- 2022.12; PI.


   l Special Fund for the cultivation of Outstanding Talents by basic scientific research operating fees of central universities:" Research on variable classification method of high-resolution image city scene based on Sparse Theme Model ", Time: 2019.07 -- 2021.06, PI.


   l Project of National Data Center for Earth Observation Science, Open Fund Project:" Production and processing of  road data set in China based on high-resolution Satellite images ", Time: 2020.09 -- 2021.08, PI.


   l  State Key Laboratory of Surveying and Mapping and Remote Sensing information Engineering,Wuhan University, Open Fund Project:" Research on detection method of urban scene change in high-resolution Remote Sensing image based on Probabilistic Theme Model ", Time: 2019.01 -- 2020.12PI.


   l  Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Open Fund Project: " High-resolution Remote Sensing image city main functional area division based on multi-semantic expression ", Time: January, 2019.01 -- December, 2020.12, PI.


   l  National Key Research  and Development Program Topics: " Land resources and ecological environment security emergency response key technologies ", Participated in the second project, " Key Emergency response Technologies for Land resources and Ecological Environment Security ", Time: 2017.07 -- 2021.07; CO-PI.


   l Outstanding Youth Science Foundation Project : " Hyperspectral Remote sensing Ground Object Recognition and Scene Understanding ", Time: 2017.1 -- 2019.12, CO-PI


   l National Natural Science Foundation of China( General Program ) : " Research on semantic understanding method of high-resolution Remote Sensing image scene based on Sparse Probability Graph Model ", Time: 2018.01 -- 2021.12, CO-PI.


   l  Youth Talent Fund of Joint Fund of The Ministry of Education for Equipment Pre-Research: " Camouflage target reconnaissance based on infrared hyperspectral Remote Sensing ", Time: 2017.01 -- 2018.12, CO-PI.


   l  Science Foundation of Outstanding Youth of Hubei Natural Science Foundation:" Semantic understanding of multi-feature scenes of high-resolution Remote Sensing images ", Time: 2016.01 -- 2018.12, CO-PI.


   l  National Development and Reform Commission Project: " Global mapping technology system and application based on high-resolution satellite images " sub-project "Land cover classification subsystem", CO-PI.

   l  Cooperation Project of State Grid Beijing Institute of Economics and Technology: " Application research of Remote Sensing technology in global energy Internet construction ", Time:  2017.01 -- 2018.11, CO-PI.


  • Education Background
  • Work Experience
2013-9 | 2018-6
  • Wuhan University
  • Photogrammetry and remote sensing
  • Postgraduate (Doctoral)
  • Doctoral Degree

  • Social Affiliations
  • Research Focus
No Content
Personal information

Associate professor
Supervisor of Master's Candidates

Honors and Titles : Two ESI Highly Cited Papers since 2017;
Excellent Advisor Award for Undergraduate Thesis in 2019,2020 and 2021;
Chairman of the International Conference on Advanced Remote Sensing in 2018;
Reviewer of TOP SCI Journals, e.g. IEEE TCYB and ISPRS.

Gender : Female

Date of Birth : 1993-05-01

Alma Mater : Wuhan University

Education Level : Faculty of Higher Institutions

Degree : 博士学位

Status : Employed

School/Department : School of Geography and Information Engineering

Date of Employment : 2018-07-01

Discipline : Remote sensing science and technology

Business Address : School of Geography and Information Engineering(529), Future City Campus, China University of Geosciences (Wuhan)

Contact Information : zhuqq@cug.edu.cn

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