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Di 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
— Nanyang Technological University, School of Electrical and Electronic Engineering, VIRTUS - IC Design Centre of Excellence, Singapore
- Di Zhu 0005
- Di Zhu 0006
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2020 – today
- 2024
- [j16]Yifan Zhang
Next track point prediction using a flexible strategy of subgraph learning on road networks. Int. J. Geogr. Inf. Sci. 38(10): 1939-1964 (2024) - [j15]Yi Wang
A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation. Inf. Fusion 104: 102149 (2024) - [c6]Sheng Wang
Edge Activating Module: Learning edge-to-edge features for mobility flow generation. GeoAI@SIGSPATIAL 2024: 11-14 - [e1]Song Gao, Gengchen Mai, Shawn D. Newsam, Lexie Yang, Dalton D. Lunga, Di Zhu, Bruno Martins, Samantha T. Arundel:
Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2024, Atlanta, GA, USA, 29 October 2024 - 1 November 2024. ACM 2024, ISBN 979-8-4007-1176-3 [contents] - [i4]Peng Luo, Di Zhu:
Uncover the nature of overlapping community in cities. CoRR abs/2402.00222 (2024) - 2023
- [j14]Di Zhu
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
A generalized heterogeneity model for spatial interpolation. Int. J. Geogr. Inf. Sci. 37(3): 634-659 (2023) - 2022
- [j12]Di Zhu
Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions. GeoInformatica 26(4): 645-676 (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
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
MetroGAN: Simulating Urban Morphology with Generative Adversarial Network. KDD 2022: 2482-2492 - [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
- [j11]Nilufer Sari Aslam
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
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
Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks. IEEE Trans. Intell. Transp. Syst. 22(12): 7474-7484 (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) - 2020
- [j8]Lun Wu, Ximeng Cheng
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
Spatial interpolation using conditional generative adversarial neural networks. Int. J. Geogr. Inf. Sci. 34(4): 735-758 (2020) - [j6]Xiaoyue Xing
Mapping Human Activity Volumes Through Remote Sensing Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 13: 5652-5668 (2020) - [j5]Fan Zhang
Uncovering inconspicuous places using social media check-ins and street view images. Comput. Environ. Urban Syst. 81: 101478 (2020)
2010 – 2019
- 2019
- [j4]Xin Yao
Visualizing spatial interaction characteristics with direction-based pattern maps. J. Vis. 22(3): 555-569 (2019) - 2018
- [j3]Xin Yao
A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends. IEEE Access 6: 44666-44675 (2018) - [j2]Shiliang Zhang, Di Zhu
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
Inferring spatial interaction patterns from sequential snapshots of spatial distributions. Int. J. Geogr. Inf. Sci. 32(4): 783-805 (2018) - [c1]Di Zhu, Yu Liu:
Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper). GIScience 2018: 73:1-73:7 - [i1]Di Zhu
Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks. CoRR abs/1808.09802 (2018)
Coauthor Index
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last updated on 2024-12-04 21:09 CET by the dblp team
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