How the Arctic is defined and perceived, or redefined, as well as how non-Arctic actors remap their geographical position and (re)identify their relationship with the Arctic region has real implications for how it is governed, as the 2020 IIASA analysis on Arctic policies reveals. Perhaps because it is distant from major political, business and media centres, the Arctic seems especially prone to external interpretations of its essential character. The theme of 2021, Defining and Mapping the Arctic: Sovereignties, Policies and Perceptions contains relevant topics that are much discussed, examined, reported and speculated in policy circles, academia, and the media. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution. For semantic distribution, statistical results show that an average of 60% of images tagged with the building’s name actually represents the building, while 69% of images depicting the building are not annotated with the building’s name. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. Spatial distribution shows how relevant images are distributed around a landmark. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them.
In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images.
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