A crowdsource approach to documenting users' preferences for landscape attributes in the proposed Appalachian Geopark Project in West Virginia, United States

Nakarmi, Ganga; Yuill, Charles; Strager, Michael P.; Butler, P. A.; Moreira, Jasmine Cardozo; Burns, Robert Ignatius

doi:10.1016/j.ijgeop.2023.05.001

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…Pretrained model A saved network previously trained on large datasets and applied directly to the task by the authors [62]. [63,64] Transfer learning A machine learning method in which a pretrained model developed for a task was reused as the starting point for a model on a second task [27]. [44,65] Other Instead of relying on pretrained models, the authors propose new architectures to train models or use other computer vision algorithms to process images.…”

Section: Model Description Examplementioning

confidence: 99%

“…Images can help visualize landscapes and avoid the misunderstandings caused by text (for example, informal idiomatic texts and pluralistic languages), thereby building a bridge between researchers and the public [26]. The introduction of photography technology in the 19th century was a milestone in the history of landscape visualization, and since then, images have gradually become a commonly used and accepted tool in landscape studies [27]. Common image-based approaches, such as questionnaires or the Photo-Elicitation Interview, involve researchers taking or providing photos; another example is Visitor-Employed Photography, which involves a small group of participants taking photos for analysis [10].…”

Section: Introductionmentioning

confidence: 99%

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Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

Ma,

Furuya

2024

Land

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This study systematically reviews 55 landscape studies that use computer vision methods to interpret social media images and summarizes their spatiotemporal distribution, research themes, method trends, platform and data selection, and limitations. The results reveal that in the past six years, social media–based landscape studies, which were in an exploratory period, entered a refined and diversified phase of automatic visual analysis of images due to the rapid development of machine learning. The efficient processing of large samples of crowdsourced images while accurately interpreting image content with the help of text content and metadata will be the main topic in the next stage of research. Finally, this study proposes a development framework based on existing gaps in four aspects, namely image data, social media platforms, computer vision methods, and ethics, to provide a reference for future research.

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Section: Model Description Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

Ma,

Furuya

2024

Land

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“…In response to public perceptual preferences for forest landscapes, some researchers have modeled the aesthetic quality of forested areas using machine learning models, including support vector machines and multilayer perceptrons, thus offering guidance for ecological technology planting in woodland landscapes [8]. The authors of [9] proposed a new method of user preference classification, integrating a machine learning model with a geographic information system (GIS), to address issues related to the public perceptual preferences for the landscape in a geopark project in the United States, effectively guiding the construction of the geopark project. Researchers have also tackled the problems associated with landscape character assessment by proposing an evaluation method for users' visual perception of a landscape based on artificial intelligence, providing supporting data for a deeper understanding of human-nature interactions and the sustainable management of the environment [10].…”

Section: Related Workmentioning

confidence: 99%

Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models

Wang,

Min,

Rui

2024

Land

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This research aimed to analyze and understand the perceived landscape preferences of lake parks (LPs) and how the public perceives and prefers these elements within the context of lake parks. The objective was to provide insights beneficial for landscape design, urban planning, and the creation of more appealing and sustainable lake parks. To achieve this, two primary methods were employed in this study: the Automated Machine Learning (Auto ML) model and the DeepLab v3+ model. To gather data for the research, 46,444 images were collected from 20 different lake parks from 2019 to 2022. Social media platforms such as Instagram, Flickr, and specific lake park community groups were tapped to source photographs from both professional photographers and the general public. According to the experimental findings, the perceived frequency of natural landscapes was 69.27%, which was higher than that of humanistic landscapes by 30.73%. The perceived intensity was also maintained between 0.09 and 0.25. The perceived frequency of water body landscapes was much greater on a macro-scale, at 73.02%, and the public had various plant preferences throughout the year. Aquatic plant landscapes with low-to-medium green visibility were preferred by the public, according to the landscape share characterization, while amusement rides with medium-to-high openness were preferred. The sky visibility of amusement rides was between 0 and 0.1 and between 0.3 and 0.5, indicating that the public preferred amusement rides with medium-to-high openness. In lake parks, the populace chose settings with less obvious architectural features. When combined, the two models used in this study are useful for identifying and analyzing the intended traits and preferences of lake parks among the general public. They also have theoretical and practical application value for directing the development of lake parks and urban landscapes.

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Systematic Overview of the Geoheritage and Geodiversity of Monogenetic Volcanic Fields of Saudi Arabia

Németh,

Moufti

2024

Geoheritage, Geoparks and Geotourism

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A crowdsource approach to documenting users' preferences for landscape attributes in the proposed Appalachian Geopark Project in West Virginia, United States

Cited by 5 publications

References 37 publications

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models

Systematic Overview of the Geoheritage and Geodiversity of Monogenetic Volcanic Fields of Saudi Arabia

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