Explainable spatially explicit geospatial artificial intelligence in urban analytics
Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph
neural networks (GNNs) have become one of the most popular methods in recent years …
neural networks (GNNs) have become one of the most popular methods in recent years …
Multi-level urban street representation with street-view imagery and hybrid semantic graph
Street-view imagery has been densely covering cities. They provide a close-up perspective
of the urban physical environment, allowing a comprehensive perception and understanding …
of the urban physical environment, allowing a comprehensive perception and understanding …
Spatiotemporal prediction of theft risk with deep inception-residual networks
Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As
crime incidents are distributed sparsely across space and time, existing deep-learning …
crime incidents are distributed sparsely across space and time, existing deep-learning …
[PDF][PDF] Explainable Spatially-Explicit GeoAI in Urban Analytics
P Liua, Y Zhanga, F Biljeckia - 2024 - researchgate.net
Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph
neural networks (GNNs) have become one of the most popular methods in recent years …
neural networks (GNNs) have become one of the most popular methods in recent years …