How well do NDVI and OpenStreetMap data capture people’s visual perceptions of urban greenspace?

R.F.L. Teeuwen*, V. Milias, Alessandro Bozzon, A. Psyllidis

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
41 Downloads (Pure)


The study of urban greenspaces typically relies on three types of data: people’s subjective perceptions collected via questionnaires, vegetation indices derived from satellite imagery, such as the Normalized Difference Vegetation Index (NDVI), and Land Use or Land Cover maps, such as OpenStreetMap (OSM). Data on people’s perceptions are essential when researching human activities, yet they scale poorly. NDVI and OSM data, on the other hand, are freely available worldwide, thus valuable for assessing cities at scale or prioritizing locations for interventions. However, it is unclear how effectively NDVI and OSM data capture people’s visual perceptions of urban greenspaces. In this work, we collect people’s visual perceptions of public spaces in three major European cities through crowdsourcing, quantitatively compare them to NDVI and OSM data, and qualitatively investigate disparities. We found that NDVI moderately correlates with perceived greenness and that not only OSM greenspaces but also pocket parks and play spaces are often considered green. Furthermore, we found that people’s perceptions correspond best to OSM data in small radius distances and NDVI data in larger radius distances and that combining NDVI and OSM data can improve identification of places in OSM that are commonly considered green. Our qualitative analysis revealed that configuration and variety of vegetation, and presence of other natural or built-up features, influence people’s perceptions of greenspace. With our findings we aim to help researchers and practitioners make more informed decisions when collecting greenspace data for their specific context, ultimately contributing to green urban environments that reflect people’s perspectives.
Original languageEnglish
Article number105009
Number of pages12
JournalLandscape and Urban Planning
Publication statusPublished - 2024


  • Urban greenspace
  • Visual perception
  • OpenStreetMap
  • NDVI
  • Crowdsourcing


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