Recognition and Classification of Homogeneous Landscape With Visitor–Employed Photography and Cloud Image Annotation API—An Example of the Riverscape in Nihonbashi, Tokyo, Japan

Abstract

Effective classification of landscape photographs is a vital step in data processing and environment analysis. With the popularity of crowdsourcing geo-information, an increasing number of studies have used geotagged photographs to visualize how people perceive and interact with destinations and explore the aesthetic, cultural, and recreational value of the areas. In recent years, machine-learning algorithms for image recognition have dramatically improved the efficiency of the assignment of keywords and provide possibilities for the automatic classification of numerous photographs. However, the applicability of such methods for the practical landscape classification is still not clear, especially for the photographs presenting a homogeneous landscape that has similar characteristics. This study developed a semi-automatic classifier for homogeneous landscape photographs by using Google Cloud Vision API and multi-level hierarchical clustering. The classifier was applied to the classification of urban riverscape photographs, which is a typical example of homogeneous landscapes in Nihonbashi, Tokyo, Japan. The riverscapes can be classified into 9 characteristic groups by the classifier and the visual impression of these groups matches well with our intuitive feeling. A confusion matrix showed that the overall accuracy was 82.61%, indicating a strong agreement between the classifier and manual classification. Therefore, the classifier is practical for classifying homogeneous riverscape photographs. Such methodology also provides the possibility of public participation in the assessing process, which, in turn, contributes to urban tourism management.

Keywords

Homogeneous Landscape / Landscape Characteristics / Image Annotation / Photograph Classification / Urban Riverscape / Machine Learning / Clusters

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