A fuzzy system for quality assurance of crowdsourced wildlife observation geodata

Hossein Vahidi, Wanglin Yan, Brian Klinkenberg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

A conceptual model for quality assurance of species occurrence observations in citizen science projects is described below. We adopted the notion of trust as an indicator of VGI quality and define the concept of trustworthiness of a VGI record as a function of three main contexts: consistency with habitat, consistency with neighbors, and the reputation of the volunteer. Using fuzzy control system the quality of an observation is quantified in terms of the level of the trustworthiness of the volunteered species observation. The architecture of the proposed system is briefly described and some results presented. Finally, our paper ends with concluding remarks and some thoughts for future research directions.

Original languageEnglish
Title of host publicationProceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
EditorsFahim Nur Cahya Bagar, Ahmad Zainudin, M. Udin Harun Al Rasyid, Hendy Briantoro, Zulhaydar Fairozal Akbar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-58
Number of pages4
ISBN (Electronic)9781538607169
DOIs
Publication statusPublished - 2017 Dec 19
Event6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 - Surabaya, Indonesia
Duration: 2017 Sept 262017 Sept 27

Publication series

NameProceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
Volume2017-January

Other

Other6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
Country/TerritoryIndonesia
CitySurabaya
Period17/9/2617/9/27

Keywords

  • Citizen Science
  • Intrinsic Data Quality Assurance
  • Species
  • Volunteered Geographic Information
  • Wildlife Observation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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