eprintid: 14583 rev_number: 13 eprint_status: archive userid: 2 dir: disk0/00/01/45/83 datestamp: 2017-05-09 15:16:19 lastmod: 2021-08-27 17:28:56 status_changed: 2017-05-09 15:16:19 type: article metadata_visibility: show item_issues_count: 1 creators_name: Bechtel, B. creators_name: Demuzere, M. creators_name: Sismanidis, P. creators_name: Fenner, D. creators_name: Brousse, O. creators_name: Beck, C. creators_name: Van Coillie, F. creators_name: Conrad, O. creators_name: Keramitsoglou, I. creators_name: Middel, A. creators_name: Mills, G. creators_name: Niyogi, D. creators_name: Otto, M. creators_name: See, L. creators_name: Verdonck, M.-L creators_id: 8571 creators_orcid: 0000-0002-2665-7065 title: Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX) ispublished: pub divisions: prog_esm keywords: Local Climate Zones (LCZs); urban climate; crowdsourcing; volunteered geographic information; classification; WUDAPT note: This article belongs to the Special Issue "Crowdsourcing Urban Data" abstract: The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets. date: 2017-05-09 publisher: Molecular Diversity Preservation International (MDPI) id_number: 10.3390/urbansci1020015 official_url: http://www.mdpi.com/journal/urbansci/special_issues/crowdsourcing creators_browse_id: 276 full_text_status: public publication: Urban Science volume: 1 number: 2 pagerange: e15 refereed: TRUE projects: Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754) coversheets_dirty: FALSE fp7_project: yes fp7_project_id: info:eu-repo/grantAgreement/EC/FP7/617754/EU//CROWDLAND fp7_type: info:eu-repo/semantics/article access_rights: info:eu-repo/semantics/openAccess citation: Bechtel, B., Demuzere, M., Sismanidis, P., Fenner, D., Brousse, O., Beck, C., Van Coillie, F., Conrad, O., et al. (2017). Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX). Urban Science 1 (2) e15. 10.3390/urbansci1020015 . document_url: https://pure.iiasa.ac.at/id/eprint/14583/1/urbansci-01-00015.pdf