{"title":"Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review","authors":"Ruochen Ma, Katsunori Furuya","doi":"10.3390/land13020181","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":37702,"journal":{"name":"Land","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Land","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.3390/land13020181","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
LandENVIRONMENTAL STUDIES-Nature and Landscape Conservation
CiteScore
4.90
自引率
23.10%
发文量
1927
期刊介绍:
Land is an international and cross-disciplinary, peer-reviewed, open access journal of land system science, landscape, soil–sediment–water systems, urban study, land–climate interactions, water–energy–land–food (WELF) nexus, biodiversity research and health nexus, land modelling and data processing, ecosystem services, and multifunctionality and sustainability etc., published monthly online by MDPI. The International Association for Landscape Ecology (IALE), European Land-use Institute (ELI), and Landscape Institute (LI) are affiliated with Land, and their members receive a discount on the article processing charge.