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

Ruochen Ma and Katsunori Furuya ()
Additional contact information
Ruochen Ma: Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan
Katsunori Furuya: Graduate School of Horticulture, Chiba University, Chiba 271-8510, Japan

Land, 2024, vol. 13, issue 2, 1-22

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.

Keywords: landscape study; social media data; computer vision; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/2/181/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/2/181/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:2:p:181-:d:1332641

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().