Mapping seasonal changes of street greenery using multi-temporal street-view images

Street greenery offers various benefits to urban environments. In regions with climatic variations among seasons, seasonal changes of vegetation may lead to fluctuations in the benefits provided by street greenery. It is vital to monitor and measure the seasonal changes of street greenery. Previous studies have analyzed changes of street greenery, mainly from an aerial view. However, aerial views may not be equivalent to residents' visual experiences. This study aims to quantitatively characterize seasonal differences in street greenery based on multi-temporal street-view images which can simulate pedestrians' view. The Gulou District in Nanjing, China, is selected for a pilot study. We collected multi-temporal street-view images through an online street-view service. Deep learning-based algorithms were used to extract seasonal street greenery from street-view images. The results revealed significant seasonal differences in street greenery in the Gulou District. We classified four street greening patterns, including (1) Deciduous and evergreen mixed pattern; (2) Deciduous-dominant pattern; (3) No-plant pattern; (4) Evergreen-dominant pattern, with distinct seasonal change characteristics. For each pattern, we explored possible adjustments in planting arrangements. Our work is a preliminary attempt, and the proposed framework could assist in future sustainable greening design and planning.