Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)

Abstract

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.

Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria

“…This information gives great insights in regards of the capacity of our climatic models to efficiently represent the local climate of urban areas in different seasonal cases. As the WUDAPT project is currently focusing on the standardization of Level 0 data (Bechtel et al ., ; Verdonck et al ., ; Zheng et al ., ) and on the development of a Level 1, which would give more detailed land use information to the model users, a question is raised concerning the portion of the forecasting error that is attributable to data sets that are currently used within the meteorological and climatological communities.…”

“…() and Bechtel et al . () or Brousse et al . (, appendix A) provide thorough descriptions of the supervised classification's steps.…”

“…More information can be found on statistical indicators of remote sensing classification accuracy in Congalton (1991). For LCZ classification processes and verifications, Bechtel and Daneke (2012), Bechtel et al (2015) and Bechtel et al (2017) or Brousse et al (2016, appendix A) provide thorough descriptions of the supervised classification's steps. In this study, Landsat 8 scenes from three seasons (winter, spring and summer) were used to compute an LCZ map over Vienna.…”

Effect of the Training Set Configuration on Sentinel-2-Based Urban Local Climate Zone Classification

“…Another solution is to adapt the LCZ scheme considering the feasibility of optical images, or a multi-level classification might be beneficial. Last but not least, negative human influence on ground truth should be weaken to guarantee the quality of the training samples across cities (Bechtel et al, 2017).…”

The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification

“…However, it is unclear what determines the final quality of a LCZ map derived with the WUDAPT level-0 methodology. Hence in 2017, the Human Influence Experiment (HUMINEX) was introduced to investigate the variability of the quality of LCZ maps, produced by different individuals using the WUDAPT methodology [23]. It aimed at identifying how large discrepancies between individual LCZ maps can be for a given city or region.…”