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Street networks are considered to be one significant component of urban structures that serve various urban functions. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality. While current research focuses on particular street assessment indices, such as accessibility and connectivity, they ignore biases in street assessment caused by differences in urban functions. To address this issue, an adaptive approach to assessing street quality from the perspective of the variation in urban functions is proposed. First, an adaptive urban function detection model is established, with street-level element segmenting using PSPNet and semantic urban function extraction using LDA topic modelling. On this basis, an urban function-driven street quality assessment is proposed to adaptively evaluate multilevel urban streets. Taking Tianhe District in Guangzhou, Guangdong Province, as the study area, experiments using street view images and points of interest (POIs) are applied to validate the proposed approach. The experiment results in a model for adaptive urban function detection with an overall accuracy of 64.3%, showing that streets with different urban functions, including traffic, commercial, and residential functions, can be assessed. The experimental results can facilitate urban function organization and urban land-use planning.

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