Tianhong Zhao
Orcid: 0000-0002-9290-2049
According to our database1,
Tianhong Zhao
authored at least 22 papers
between 2019 and 2026.
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Bibliography
2026
ST-Camba: A decoupled-free spatiotemporal graph fusion state space model with linear complexity for efficient traffic forecasting.
Inf. Fusion, 2026
2025
Planning for Cooler Cities: A Multimodal AI Framework for Predicting and Mitigating Urban Heat Stress through Urban Landscape Transformation.
CoRR, July, 2025
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach.
CoRR, May, 2025
OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery.
CoRR, April, 2025
Quantifying seasonal bias in street view imagery for urban form assessment: A global analysis of 40 cities.
Comput. Environ. Urban Syst., 2025
ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science.
Comput. Environ. Urban Syst., 2025
Disentangling the hourly dynamics of mixed urban function: A multimodal fusion perspective using dynamic graphs.
Inf. Fusion, 2025
Spatiotemporal Analysis of Urban Vitality and Its Drivers from a Human Mobility Perspective.
ISPRS Int. J. Geo Inf., 2025
SemiGPS: GraphGPS-based Semi-supervised Graph Learning for Sector-Specific GDP Mapping.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025
Semantics-Guided Dynamic Hypergraph Network for Human Mobility Nowcasting in Disaster.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025
2024
Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities.
Int. J. Geogr. Inf. Sci., May, 2024
Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning.
Int. J. Geogr. Inf. Sci., February, 2024
2023
Incorporating multimodal context information into traffic speed forecasting through graph deep learning.
Int. J. Geogr. Inf. Sci., September, 2023
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, August, 2023
Comput. Environ. Urban Syst., 2023
Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus.
Int. J. Geogr. Inf. Sci., 2023
2022
Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction.
Comput. Environ. Urban Syst., 2022
IEEE Trans. Intell. Transp. Syst., 2022
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., 2022
2021
Proceedings of the SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems, 2021
2020
2019
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