[HTML][HTML] A review of spatially-explicit GeoAI applications in Urban Geography
P Liu, F Biljecki - International Journal of Applied Earth Observation and …, 2022 - Elsevier
Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
A survey on deep learning for human mobility
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …
such as disease spreading, urban planning, well-being, pollution, and more. The …
GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond
Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-
quality data, as well as advances in both hardware and software to efficiently process these …
quality data, as well as advances in both hardware and software to efficiently process these …
Deep learning for road traffic forecasting: Does it make a difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …
This has also been the case of Intelligent Transportation Systems, in which several areas …
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
As an important task for the management of bike sharing systems, accurate forecast of travel
demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In …
demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In …
Short-term traffic prediction with deep neural networks: A survey
In modern transportation systems, an enormous amount of traffic data is generated every
day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep …
day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep …
Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction
Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to
provide comfortable and flexible bus services. The built environment, including land use …
provide comfortable and flexible bus services. The built environment, including land use …
Applying hybrid LSTM-GRU model based on heterogeneous data sources for traffic speed prediction in urban areas
With the advent of the Internet of Things (IoT), it has become possible to have a variety of
data sets generated through numerous types of sensors deployed across large urban areas …
data sets generated through numerous types of sensors deployed across large urban areas …
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Short-term traffic forecasting on large street networks is significant in transportation and
urban management, such as real-time route guidance and congestion alleviation …
urban management, such as real-time route guidance and congestion alleviation …
Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks
Dynamic human activity intensity information is of great importance in many location-based
applications. However, two limitations remain in the prediction of human activity intensity …
applications. However, two limitations remain in the prediction of human activity intensity …