Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 198, P. 153 - 168

Published: March 16, 2023

Language: Английский

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Structural analysis and vulnerability assessment of the European LNG maritime supply chain network (2018–2020)

Ocean & Coastal Management, Journal Year: 2024, Volume and Issue: 253, P. 107126 - 107126

Published: April 9, 2024

Language: Английский

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Explainable spatially explicit geospatial artificial intelligence in urban analytics

Environment and Planning B Urban Analytics and City Science, Journal Year: 2023, Volume and Issue: 51(5), P. 1104 - 1123

Published: Sept. 29, 2023

Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph neural networks (GNNs) have become one of the most popular methods recent years. However, along with success GNNs, black box nature AI models has led to various concerns (e.g. algorithmic bias and model misuse) regarding their adoption particularly when studying socio-economics high transparency a crucial component social justice. Therefore, desire for increased explainability interpretability attracted increasing research interest. This article proposes an explainable spatially explicit GeoAI-based analytical method that combines convolutional network (GCN) graph-based (XAI) method, called GNNExplainer. Here, we showcase ability our proposed two studies within analytics: traffic volume prediction population estimation tasks node classification classification, respectively. For these tasks, used Street View Imagery (SVI), trending data source analytics. We extracted semantic information from images assigned them as features roads. The GCN first provided reasonable predictions related by encoding roads nodes connectivities graphs. GNNExplainer then offered insights into how certain are made. Through such process, practical conclusions can be derived phenomena studied here. In this paper also set out path developing XAI future studies.

Language: Английский

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Predicting the network shift of large urban agglomerations in China using the deep-learning gravity model: A perspective of population migration

Cities, Journal Year: 2023, Volume and Issue: 145, P. 104680 - 104680

Published: Dec. 2, 2023

Language: Английский

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Migratable urban street scene sensing method based on vision language pre-trained model

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 113, P. 102989 - 102989

Published: Sept. 1, 2022

We propose a geographically reproducible approach to urban scene sensing based on large-scale pre-trained models. With the rise of GeoAI research, many high-quality observation datasets and deep learning models have emerged. However, geospatial heterogeneity makes these resources challenging share migrate new application scenarios. This paper introduces vision language semantic model for street view image analysis as an example. bridges boundaries data formats under location coupling, allowing acquisition text-image objective descriptions in physical space from human perspective, including entities, entity attributes, relationships between entities. Besides, we proposed SFT-BERT extract text feature sets 10 land use categories 8,923 scenes Wuhan. The results show that our method outperforms seven baseline models, computer vision, improves 15% compared traditional methods, demonstrating potential pre-train & fine-tune paradigm GIS spatial analysis. Our could also be reused other cities, more accurate judgments obtained by inputting images different angles. code is shared at: github.com/yemanzhongting/CityCaption.

Language: Английский

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Short-term subway passenger flow forecasting approach based on multi-source data fusion

Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 121109 - 121109

Published: Sept. 1, 2024

Language: Английский

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Multi-level urban street representation with street-view imagery and hybrid semantic graph

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 218, P. 19 - 32

Published: Oct. 18, 2024

Language: Английский

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Understanding the City Networks: An Analysis from China’s Inter-city Population Migration

Applied Spatial Analysis and Policy, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 10, 2025

Language: Английский

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A bottom-up approach of knowledge graph modelling for urban underground public spaces: Insights into public cognition

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 163, P. 106710 - 106710

Published: April 30, 2025

Language: Английский

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HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 140, P. 104565 - 104565

Published: May 14, 2025

Language: Английский

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