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ORCIDDi Zhu 0004
Person information
- affiliation: University of Minnesota, USA
- affiliation (former): Peiking University, Institute of Remote Sensing and GIS, Beijing, China
Other persons with the same name
- Di Zhu — disambiguation page
- Di Zhu 0001 — Chinese Academy of Science, National Space Science Center, Key Laboratory of Microwave Remote Sensing, Beijing, China
- Di Zhu 0002 — University of Southern California, Department of Electrical Engineering, Los Angeles, CA, USA
- Di Zhu 0003
![[0000-0001-5086-0971]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-5086-0971")
— Nanyang Technological University, School of Electrical and Electronic Engineering, VIRTUS - IC Design Centre of Excellence, Singapore - Di Zhu 0005 — Stevens Institute of Technology, Hoboken, NJ, USA
- Di Zhu 0006 — North Carolina State University, Department of Mechanical Engineering, Raleigh, NC, USA
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Journal Articles
- 2024
[j15]Yi Wang, Di Zhu:
A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation. Inf. Fusion 104: 102149 (2024)- 2023
- [j14]Di Zhu, Yu Liu, Xin Yao, Manfred M. Fischer:
Correction to: Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica 27(3): 641-642 (2023) - [j13]Peng Luo, Yongze Song, Di Zhu, Junyi Cheng, Liqiu Meng:
A generalized heterogeneity model for spatial interpolation. Int. J. Geogr. Inf. Sci. 37(3): 634-659 (2023) - 2022
- [j12]Di Zhu, Yu Liu, Xin Yao, Manfred M. Fischer:
Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica 26(4): 645-676 (2022) - 2021
- [j11]Nilufer Sari Aslam, Di Zhu, Tao Cheng, Mohamed R. Ibrahim, Yang Zhang:
Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London. Ann. GIS 27(1): 29-41 (2021) - [j10]Xiao Huang, Di Zhu, Fan Zhang, Tao Liu, Xiao Li, Lei Zou:
Sensing Population Distribution from Satellite Imagery Via Deep Learning: Model Selection, Neighboring Effects, and Systematic Biases. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 5137-5151 (2021) - [j9]Xin Yao, Yong Gao, Di Zhu, Ed Manley, Jiaoe Wang, Yu Liu:
Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks. IEEE Trans. Intell. Transp. Syst. 22(12): 7474-7484 (2021) - 2020
- [j8]Lun Wu, Ximeng Cheng, Chaogui Kang, Di Zhu, Zhou Huang, Yu Liu:
A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data. Int. J. Digit. Earth 13(6): 708-726 (2020) - [j7]Di Zhu, Ximeng Cheng, Fan Zhang, Xin Yao, Yong Gao, Yu Liu:
Spatial interpolation using conditional generative adversarial neural networks. Int. J. Geogr. Inf. Sci. 34(4): 735-758 (2020) - [j6]Xiaoyue Xing, Zhou Huang, Ximeng Cheng, Di Zhu, Chaogui Kang, Fan Zhang, Yu Liu:
Mapping Human Activity Volumes Through Remote Sensing Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 13: 5652-5668 (2020) - [j5]Fan Zhang, Jinyan Zu, Mingyuan Hu, Di Zhu, Yuhao Kang, Song Gao, Yi Zhang, Zhou Huang:
Uncovering inconspicuous places using social media check-ins and street view images. Comput. Environ. Urban Syst. 81: 101478 (2020) - 2019
- [j4]Xin Yao, Lun Wu, Di Zhu, Yong Gao, Yu Liu:
Visualizing spatial interaction characteristics with direction-based pattern maps. J. Vis. 22(3): 555-569 (2019) - 2018
- [j3]Xin Yao, Di Zhu, Yong Gao, Lun Wu, Pengcheng Zhang, Yu Liu:
A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends. IEEE Access 6: 44666-44675 (2018) - [j2]Shiliang Zhang, Di Zhu, Xin Yao, Ximeng Cheng, Huagui He, Yu Liu:
The Scale Effect on Spatial Interaction Patterns: An Empirical Study Using Taxi O-D data of Beijing and Shanghai. IEEE Access 6: 51994-52003 (2018) - [j1]Di Zhu, Zhou Huang, Li Shi, Lun Wu, Yu Liu:
Inferring spatial interaction patterns from sequential snapshots of spatial distributions. Int. J. Geogr. Inf. Sci. 32(4): 783-805 (2018)
Conference and Workshop Papers
- 2022
- [c5]Peng Luo, Di Zhu:
Sensing overlapping geospatial communities from human movements using graph affiliation generation models. GeoAI@SIGSPATIAL 2022: 1-9 - [c4]Di Zhu, Song Gao, Guofeng Cao:
Towards the intelligent era of spatial analysis and modeling. GeoAI@SIGSPATIAL 2022: 10-13 - [c3]Yi Wang, Di Zhu:
SHGCN: a hypergraph-based deep learning model for spatiotemporal traffic flow prediction. GeoAI@SIGSPATIAL 2022: 30-39 - [c2]Weiyu Zhang, Yiyang Ma, Di Zhu, Lei Dong, Yu Liu:
MetroGAN: Simulating Urban Morphology with Generative Adversarial Network. KDD 2022: 2482-2492 - 2018
- [c1]Di Zhu, Yu Liu:
Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper). GIScience 2018: 73:1-73:7
Informal and Other Publications
- 2024
- [i4]Peng Luo, Di Zhu:
Uncover the nature of overlapping community in cities. CoRR abs/2402.00222 (2024) - 2022
- [i3]Weiyu Zhang, Yiyang Ma, Di Zhu, Lei Dong, Yu Liu:
MetroGAN: Simulating Urban Morphology with Generative Adversarial Network. CoRR abs/2207.02590 (2022) - 2021
- [i2]Xiao Huang, Di Zhu, Fan Zhang, Tao Liu, Xiao Li, Lei Zou:
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases. CoRR abs/2103.02155 (2021) - 2018
- [i1]Di Zhu, Yu Liu:
Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks. CoRR abs/1808.09802 (2018)
Coauthor Index
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