Architecture form has been one of the hot areas in the field of architectural design, which reflects regional architectural features to some extent. However, most of the existing methods for architecture form belong to the field of qualitative analysis. Accordingly, quantitative methods are urgently required to extract regional architectural style, identify architecture form, and to and further provide the quantitative evaluation. Based on machine learning technology, this paper proposes a novel method to quantify the feature, form, and evaluation of regional architectures. First, we construct a training dataset—the Chinese Ancient Architecture Image Dataset (CAAID), in which each image is labeled by some experts as having at least one of three typical features such as “High Pedestal”, “Deep Eave” and “Elegant Gable”. Second, the CAAID is used to train our neural network model to identify three kinds of architectural features. In order to reveal the traditional forms of regional architecture in Hubei, we built the Hubei Architectural Heritage Image Dataset (HAHID) as our object dataset, in which we collected architectural images from four different regions including southeast, northeast, southwest, and northwest Hubei. Our object dataset is then fed into our neural network model to predict the typical features for those four regions in Hubei. The obtained quantitative results show that the feature identification of the architectural form is consistent with that of regional architectures in Hubei. Moreover, we can observe from the quantitative results that four geographic regions in Hubei show variation; for instance, the feature of the ‘elegant gable’ in southeastern Hubei is more evident, while the “Deep Eave” in the northwest is more evident. In addition, some new building images are selected to feed into our neural network model and the output quantitative results can effectively identify the corresponding feature style of regional architectures in Hubei. Therefore, our proposed method based on machine learning can be used not only as a quantitative tool to extract features of regional architectures, but also as an effective approach to evaluate architecture forms in the urban renewal process.

Over the past decade, enhancing the quality of cities and building vibrant urban streets has become a hot topic in urban planning in China. Although there are many studies on how the built environment affects street vitality, the unique built environment of the street space in historic areas, as the core node of the city, has not been fully explored. This study constructs an association model between the street built environment (SBE) and street vitality in historic areas and evaluates the influence of SBE on street vitality by spatial analysis and statistical analysis methods using POI data, road network data, and Baidu heat map data, taking Wuhan, China, as an example. The results showed that (1) appropriate built environment development intensity, street width-to-height ratio, and facade ratio of historic buildings on the street frontage all can promote street vitality; (2) the spatial distribution of historic buildings converted to commercial functions in historic areas has a high consistency with the spatial distribution of street vitality, and the consistency is significantly higher than that of general urban streets; (3) historic buildings converted to residential functions and those in vacancy or under renovation in historic areas have a significant inhibitory effect on street vitality; and (4) the spatial distribution of transportation facilities and the spatial distribution of street vitality are mutually exclusive in historic areas. This study proposes a method for studying the SBE and street vitality in historic areas and initially explores the relationship between the influences of the SBE on street vitality in historic areas. Since the functional replacement of historic buildings can affect the street vibrancy in historic areas, our findings suggest moderating commercial renovation rather than simply repairing or maintaining the status for enhancing the street vitality. Moreover, the intervention of transportation facilities will reduce the street vitality in historic areas, which provides a basis for the strategy of renewing historic areas into pedestrian street spaces.