Resumen
Street space quality assessment refers to the extraction and appropriate evaluation of the space quality information of urban streets, which is usually employed to improve the quality of urban planning and management. Compared to traditional approaches relying on expert knowledge, the advances of big data collection and analysis technologies provide an alternative for assessing street space more precisely. With street view imagery (SVI), points of interest (POI) and comment data from social media, this study evaluates street space quality from the perspective of exploring and discussing the relationship among street vitality, service facilities and built environment. Firstly, a transfer-learning-based framework is employed for SVI semantic segmentation to quantify the street built environment. Then, we use POI data to identify different urban functions that streets serve, and comment data are utilized to investigate urban vitality composition and integrate it with different urban functions associated with streets. Finally, a function-driven street space quality assessment approach is established. To examine its applicability and performance, the proposed method is experimented with data from part area in Xiamen, China. The output is compared to results based on expert opinion using the correlation analysis method. Results show that the proposed assessment approach designed in this study is in accordance with the validation data, with the overall R2 value being greater than 0.6. In particular, the proposed method shows better performance in scenic land and mixed functional streets with R2 value being greater than 0.8. This method is expected to be an efficient tool for discovering problems and optimizing urban planning and management.