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Tianhong Zhao
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2020 – today
- 2024
- [j11]Wei Tu, Haoyu Ye, Ke Mai, Meng Zhou, Jincheng Jiang, Tianhong Zhao, Shengao Yi
, Qingquan Li:
Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning. Int. J. Geogr. Inf. Sci. 38(2): 216-242 (2024) - [j10]Ding Ma
Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities. Int. J. Geogr. Inf. Sci. 38(5): 931-955 (2024) - 2023
- [j9]Filip Biljecki
Sensitivity of measuring the urban form and greenery using street-level imagery: A comparative study of approaches and visual perspectives. Int. J. Appl. Earth Obs. Geoinformation 122: 103385 (2023) - [j8]Tianhong Zhao
Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus. Int. J. Geogr. Inf. Sci. 37(7): 1555-1581 (2023) - [j7]Yatao Zhang
Incorporating multimodal context information into traffic speed forecasting through graph deep learning. Int. J. Geogr. Inf. Sci. 37(9): 1909-1935 (2023) - [j6]Tianhong Zhao, Xiucheng Liang
Sensing urban soundscapes from street view imagery. Comput. Environ. Urban Syst. 99: 101915 (2023) - 2022
- [j5]Chao Yang
Correction: Yang et al. Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong-Hong Kong-Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data. Remote Sens. 2019, 11, 2215. Remote. Sens. 14(16): 4108 (2022) - [j4]Tianhong Zhao
Optimizing Living Material Delivery During the COVID-19 Outbreak. IEEE Trans. Intell. Transp. Syst. 23(7): 6709-6719 (2022) - [j3]Tianhong Zhao
Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction. Comput. Environ. Urban Syst. 94: 101776 (2022) - 2021
- [c1]Yu Gu
Collaboratively inspect large-area sewer pipe networks using pipe robotic capsules. SIGSPATIAL/GIS 2021: 211-220 - 2020
- [j2]Wei Tu
OCD: Online Crowdsourced Delivery for On-Demand Food. IEEE Internet Things J. 7(8): 6842-6854 (2020)
2010 – 2019
- 2019
- [j1]Chao Yang
Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong-Hong Kong-Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data. Remote. Sens. 11(19): 2215 (2019)
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last updated on 2024-10-07 21:17 CEST by the dblp team
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