Urban air pollution is a critical global issue that negatively affects public health and the environment. Traditional air quality monitoring relies on physical monitoring stations, which are limited in geographic coverage and expensive to maintain. T... Read More about Colours-of-the-Wind (COLD): A novel framework for image-based air quality prediction using deep learning and spatial-temporal analytics.
All Outputs (3)
Time series forecasting for air quality with structured and unstructured data using artificial neural networks (2025)
Journal Article
Various machine learning algorithms exist to predict air quality, but they can only analyse structured data gathered from monitoring stations. However, the concentration of certain pollutants, such as PM2.5 and PM10, can be visually significant when... Read More about Time series forecasting for air quality with structured and unstructured data using artificial neural networks.
A framework for the estimation of air quality by applying meteorological images: Colours-of-the-Wind (COLD) (2023)
Journal Article
This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation... Read More about A framework for the estimation of air quality by applying meteorological images: Colours-of-the-Wind (COLD).