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Found 8 papers, 1 papers with code
Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
Sentence transformers are language models designed to perform semantic search.
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity.
Re-designing cities with conditional adversarial networks
This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating 1) an urban intervention policy, 2) an attention map that localises where intervention is needed, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536) after implementing the intervention.
Generative Adversarial Network
Image-to-Image Translation
+2
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode.
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice.
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
How can deep learning and Artificial Intelligence (AI) untangle the complexities of informality to advance urban modelling and our understanding of cities?
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present.
Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification
Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country.
Ranked #1 on
Trajectory Prediction
on GPS
