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Free keywords:
OpenStreetMap; MapSwipe; data completeness; disaster management; exposure; volunteered geographic information; data quality
Abstract:
Natural hazards threaten millions of people all over the world. To address this risk,
exposure and vulnerability models with high resolution data are essential. However, in many areas
of the world, exposure models are rather coarse and are aggregated over large areas. Although
OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level,
the completeness of OSM building footprints is still heterogeneous. We present an approach to
close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers
swipe through satellite images of a region collecting user feedback on classification tasks. For our
application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no
building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness
feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic
approach to quantify completeness and with the prediction of an existing model. Our results show
that the crowd-sourced approach yields a reasonable classification performance of the completeness of
OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent
estimates for the case study regions while the other two approaches showed a higher variability. Our
study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”,
especially in areas with a high OSM building density. Another factor that influenced the classification
performance was the level of alignment of the OSM layer with the satellite imagery.
