Portraying on-road CO 2 concentrations using street view panoramas and ensemble learning

A significant reduction in carbon dioxide (CO ) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO concentrations. >100 thousand sample pairs that matched "street view-CO concentration" were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO concentrations. After performing EL fusion modeling, the predictive R in the test set exceeded 90 %, and the mean absolute error (MAE) was