Personal Information

Shouhang DU

Lecturer

Email：dush@cumtb.edu.cn

Research Interests

(1) Intelligent Understanding and Analysis of Urban Big Data.

(2) Deep Learning and Remote Sensing Applications.

(3) Natural Resources Monitoring and Ecology Assessment.

Education/Work Background

2010.09-2014.07，School of Geography and Information Engineering, China University of Geosciences-Wuhan, Bachelor;

2014.09-2017.07，School of Geosciences and Info-physics, Central South University, Master；

2017.09-2021.07，School of Earth and Space Sciences, Peking University, PhD；

2021.07-Now，School of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Lecturer.

Teaching Courses

Fundamentals of Surveying

Application of Artificial Intelligence on Remote Sensing

Key Research Funding

[1] Research on multi-dimensional feature fusion of multi-modal data and fine extraction of urban functional zones. National Natural Science Foundation of China, 2023-2025, PI

[2] Urban functional zone extraction based on multimodal data fusion and self-supervised comparative learning. China Postdoctoral Science Foundation, 2023-2024, PI

[3] Fine-grained extraction of urban functional zones by integrating remote sensing and multi-source geographic data. China Postdoctoral Science Foundation, 2021-2023, PI

Honors

2022, I won the second prize in geographic information technology progress.

2021, I was awarded the “Outstanding Graduate of Peking University”.

2020, I was awarded the “National Scholarships for Doctoral Students”.

2018, I was awarded the “Excellent report of the 8th National Academic Forum for Doctoral Students in Geographic Information Science.”.

Selected Publications

[1] Du, S., Du, S., Liu, B., & Zhang, X. (2021). Mapping large-scale and fine-grained urban functional zones from VHR images using a multi-scale semantic segmentation network and object based approach. Remote Sensing of Environment, 261, 112480. (SCI, IF= 13.5)

[2] Du, S., Zhang, Y., Zou, Z., Xu, S., He, X., & Chen, S. (2017). Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 294-307. (SCI, IF=12.7)

[3] Du, S., Du, S., Liu, B., Zhang, X., & Zheng, Z. (2020). Large-scale urban functional zone mapping by integrating remote sensing images and open social data. GIScience & Remote Sensing, 57(3), 411-430. (SCI, IF=6.7)

[4] Du, S., Xing, J., Li, J., Du, S., Zhang, C., & Sun, Y. (2022). Open-pit mine extractionfrom very high resolution remote sensing images using OM-DeepLab. Natural Resources Research, 1-22. (SCI, IF=5.4)

[5] Du, S., Du, S., Liu, B., & Zhang, X. (2021). Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images. International Journal of Digital Earth, 14(3), 357-378. (SCI, IF=5.1)

[6] Du, S., Du, S., Liu, B., & Zhang, X. (2019). Context-enabled extraction of large-scale urban functional zones from very-high-resolution images: A multiscale segmentation approach. Remote Sensing, 11(16), 1902. (SCI, IF=5.0)

[7] Du, S., Zhang, Y., Qin, R., Yang, Z., Zou, Z., Tang, Y., & Fan, C. (2016). Building change detection using old aerial images and new LiDAR data. Remote Sensing, 8(12), 1030. (SCI, IF=5.0)

[8] Du, S., Li, W., Li, J., Du, S., Zhang, C., & Sun, Y. (2022). Open-pit mine change detection from high resolution remote sensing images using DA-UNet++ and object-based approach. International Journal of Mining, Reclamation and Environment, 1-24. (SCI, IF=2.4)

[9] Du, S., Xing, J., Du, S., Cui, X., Xiao, X., Li, W., & Wang, S. (2023). IMG2HEIGHT: Height estimation from single remote sensing image using a deep convolutional encoder-decoder network. International Journal of Remote Sensing. (SCI, IF=3.4)

[10] Li, J., Xing, J., Du, S.*, Du, S., Zhang, C., & Li, W. (2022). Change detection of open-pit mine based on siamese multi-scale network (2022). IEEE Geoscience and Remote Sensing Letters. (SCI, IF=4.8)

[11] Wang, C., Du, S.*, Sun, W., & Fan, D. (2023). Self-supervised Learning for High-resolution Remote Sensing Images Change Detection with Variational Information Bottleneck. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (SCI, IF=5.5)