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In remote sensing image retrieval task, deep metric learning loss is an effective method for learning semantic similarity relationships between images. However, most deep metric learning losses treat different ranked images equally. Therefore, we propose a novel score-ranking smooth average precision (SRSAP) loss to improve the retrieval performance. Firstly, the score-ranking (SR) loss builds an ensemble-based similarity structure by using all images. Secondly, we utilize smooth AP to assign more weight to higher-ranked images. Finally, the joint loss function is utilized to fine-tune the network. The experiments show that SRSAP loss is distinctly superior to usual deep metric losses. [ABSTRACT FROM AUTHOR]

Public acceptability is a major concern for road pricing schemes in Western countries but has not yet been sufficiently studied in the context of Chinese cities, a number of which are considering the introduction of such travel constraint measures. This study explores factors influencing public acceptability of a proposed congestion charge in the City of Beijing. In so doing, the study focuses on understanding the appropriateness of Western frameworks for assessing public acceptability in the Chinese context. Through literature review and focus groups a survey to test different public acceptability constructs was developed (N = 1104). A Structural Equation Model was used to analyze relationships that exist among the different aspects of public acceptability. The results demonstrate that public acceptability is dominantly influenced by the level of trust toward the Government and experts. Various determinants in the Western context, such as access to information and perceived effectiveness were not found to have a significant impact on public acceptability. The results imply that public acceptability of congestion charging in the Chinese context has a stronger resonance with wider social issues such as equity than more specific transport problems such as congestion. As such, attempting to present evidence on the anticipated effectiveness of the policy in alleviating congestion and smog may not make the policy more acceptable to the public. The overall inference of the study is that contextual factors are more important than has been previously considered within public acceptability studies. [ABSTRACT FROM AUTHOR]