[HTML][HTML] Machine learning for spatial analyses in urban areas: a scoping review
The challenges for sustainable cities to protect the environment, ensure economic growth,
and maintain social justice have been widely recognized. Along with the digitization …
and maintain social justice have been widely recognized. Along with the digitization …
A survey on spatio-temporal data analytics systems
Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …
and applications, and the importance of extracted knowledge from spatio-temporal data to …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method
Land-use classification plays an important role in urban planning and resource allocation
and had contributed to a wide range of urban studies and investigations. With the …
and had contributed to a wide range of urban studies and investigations. With the …
Characterizing metro stations via urban function: Thematic evidence from transit-oriented development (TOD) in Hong Kong
The strategies using transit-oriented development (TOD) to optimize transportation
sustainability have been implemented in many metropolitan areas and extended beyond the …
sustainability have been implemented in many metropolitan areas and extended beyond the …
Semi-supervised air quality forecasting via self-supervised hierarchical graph neural network
Predicting air quality in fine spatiotemporal granularity is of great importance for air pollution
control and urban sustainability. However, existing studies are either focused on predicting …
control and urban sustainability. However, existing studies are either focused on predicting …
RMGen: A tri-layer vehicular trajectory data generation model exploring urban region division and mobility pattern
As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has
attracted extensive attention in the research field. To deeply study the IoV and build a …
attracted extensive attention in the research field. To deeply study the IoV and build a …
Application of a graph convolutional network with visual and semantic features to classify urban scenes
Urban scenes consist of visual and semantic features and exhibit spatial relationships
among land-use types (eg industrial areas are far away from the residential zones). This …
among land-use types (eg industrial areas are far away from the residential zones). This …
[HTML][HTML] Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method
M Yang, B Kong, R Dang, X Yan - … Journal of Applied Earth Observation and …, 2022 - Elsevier
The automatic classification of urban functional regions is vital for urban planning and
governance. The current methods mainly rely on single remote sensing image data or social …
governance. The current methods mainly rely on single remote sensing image data or social …
Jointly contrastive representation learning on road network and trajectory
Road network and trajectory representation learning are essential for traffic systems since
the learned representation can be directly used in various downstream tasks (eg, traffic …
the learned representation can be directly used in various downstream tasks (eg, traffic …