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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.
Highlights
The role of cities as drivers of global environmental change and as places that are uniquely exposed to a range of natural hazards has highlighted a data gap
This paper provides the first results of HUMan INfluence EXperiment (HUMINEX), organized as follows: (i) in Section 2 the experiment is introduced; (ii) in Section 3 the data collection for the experiment and the analysis methods are outlined; (iii) the results obtained are presented in Section 4; followed by (iv) a discussion of the implications of the findings for future Local Climate Zones (LCZ)
We assessed the accuracy of the different iterations by diverse accuracy measures (Section 4.3); and the added value of combining multiple training areas (TAs) datasets to create a single LCZ map was assessed (Section 4.4)
Summary
The role of cities as drivers of global environmental change and as places that are uniquely exposed to a range of natural hazards (both current and projected) has highlighted a data gap. While there are numerous studies on urban growth [1,2,3], building urban resilience [4,5,6], and on urban analytics and smart cities [7,8,9], there is a dearth of information on the place-specific character of urban landscapes worldwide This information is needed to make informed decisions about the nature of urban risks, to provide a basis for planning of sustainable cities, to transfer knowledge between cities, to run increasingly sophisticated models on urban impacts on ecosystems, and to link global/regional environmental change to city outcomes. Satellite-based sensors are ideally suited to this task and have been used to generate global urban masks (that is, the extent of urban cover) [10,11] but these have not provided any detail on the internal make-up of cities [12]
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