Liveability Factor Prediction

Liveability factor prediction aims to quantify and forecast the quality of life in urban environments using various data sources. Current research focuses on leveraging computer vision techniques, particularly convolutional neural networks, to analyze imagery (street-level and aerial) and correlate it with established liveability scores, often incorporating time-series analysis to track changes over time. Challenges include handling data imbalances and accounting for confounding factors that influence image characteristics independently of liveability. Improved prediction models could inform urban planning and resource allocation, leading to more livable and sustainable cities.