Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has 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 in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility. Here, we propose a framework, ActivityNET, using Machine Learning (ML) algorithms to predict passengers’ trip purpose from Smart Card (SC) data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals’ daily travel patterns from smart card data and combining them with POIs using the proposed “activity-POIs consolidation algorithm”. Phase II feeds the extracted features into an Artificial Neural Network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

In recent years, deep learning and computer vision have been applied to solve complex problems across many domains. In urban studies, these technologies have been instrumental in the development of smart cities and autonomous vehicles. However, a knowledge gap is present when it comes to informal urban regions in less developed countries. How can deep learning and artificial intelligence untangle the complexities of informality to advance urban modelling? In this paper, we introduce a framework for multipurpose realistic-dynamic urban modelling using deep convolutional neural networks. The purpose of the framework is twofold: (1) to sense and detect informality and slums in urban scenes from aerial and street-level images and (2) to detect pedestrian and transport modes. The model has been trained on images of urban scenes in cities across the globe. The framework shows strong validation performance in the identification of planned and unplanned regions, despite broad variations in the classified images. The algorithms of the URBAN-i model are coded in Python and the trained models can be applied to images of any urban setting, including informal settlements and slum regions.