Including Users’ Semantics in Evaluating the Credibility of Crowdsourced Landscape Descriptions

Abstract

ABSTRACT:Landscape refers to the visible features of an area of land often considered in terms of their aesthetic appeal. The spatial configurations, composition conditions and perception situations of a landscape may be described by people, which reveal how they see and percept the landscape; thus they are different from person to person depending on the way they think or experience their surroundings. Especially, when the landscape descriptions are acquired through crowdsourcing processes, it is affected by users' semantics, which results in descriptions with varying credibilities. This paper proposes an approach to consider the user's semantics in evaluating the creditability of crowdsourced landscape descriptions.

Volunteered Geographic Information Users Contributions Pattern and its Impact on Information Quality

“…Such challenges can be overcome through new modes of inquiry. Through a VGI case study on semantics used in landscape description, Forati et al [37] illustrated that there is a correlation between VGI contributors' demographic characteristics (age, gender) and the quality of their VGI. Expanding on their results, Forati and Karimipour [32] presented an algorithm that can more efficiently accredit users and their edits based on factors affecting users' performances and evaluate the quality of VGI.…”

Environmental Condition and Its Impact on Landscape Description by Salient Element

“…Therefore, there is too much data thanks to diversity of conditions observer faces during describing a landscape; however, the concern of reliability of gathered data is still raised (Flanagin and Metzger, 2008). Actually, when it comes to reliability and credibility of crowedsourced data, knowing the human-environment relations is the first step to create a participatory environment by which individuals would easily air their view about different landscape and share it with each other (Forati et al, 2015). This condition improves the opportunity of having more reliable data to use them in further research.…”