A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images

In the past decade, using Street View images and machine learning to measure human perception has become a mainstream research approach in urban science. However, this approach using only image-shallow information makes it difficult to comprehensively understand the deep semantic features of human perception of a scene. In this study, we proposed a new framework based on a pre-train natural language model to understand the relationship between human perception and the sense of a scene. Firstly, Place Pulse 2.0 was used as our base dataset, which contains a variety of human-perceived labels, namely, beautiful, safe, wealthy, depressing, boring, and lively. An image captioning network was used to extract the description information of each street view image. Secondly, a pre-trained BERT model was finetuning and added a regression function for six human perceptual dimensions. Furthermore, we compared the performance of five traditional regression methods with our approach and conducted a migration experiment in Hong Kong. Our results show that human perception scoring by deep semantic features performed better than previous studies by machine learning methods with shallow features. The use of deep scene semantic features provides new ideas for subsequent human perception research, as well as better explanatory power in the face of spatial heterogeneity.

A new approach for generation of generalized basic probability assignment in the evidence theory

The process of information fusion needs to deal with a large number of uncertain information with multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster-Shafer evidence theory is widely used in multi-source information fusion owing to its effectiveness in data fusion. Information sources have an important impact on multi-source information fusion in an environment of complex, unstable, uncertain, and incomplete characteristics. To address multi-source information fusion problem, this paper considers the situation of uncertain information modeling from the closed world to the open world assumption and studies the generation of basic probability assignment (BPA) with incomplete information. In this paper, a new method is proposed to generate generalized basic probability assignment (GBPA) based on the triangular fuzzy number model under the open world assumption. The proposed method can not only be used in different complex environments simply and flexibly, but also have less information loss in information processing. Finally, a series of comprehensive experiments basing on the UCI data sets are used to verify the rationality and superiority of the proposed method.