Quality assessment for building footprints data on OpenStreetMap

By Hongchao Fan, Alexander Zipf, Qing Fu, Pascal Neis. International Journal of Geographical Information Science, 2014

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A review of volunteered geographic information quality assessment methods (2017, background): “Positional Accuracy In the works of Kounadi (2009), Ather (2009), Haklay (2010) Kounadi 2009, Ather 2009, Ciep(cid:32)luch et al. 2011, Hecht et al. 2013, Jackson et al. 2013, Fan et al. 2014, Tenney 2014, Brando and Bucher 2010), geometric accuracy (Girres and Touya 2010).”
An improved LOD specification for 3D building models (2016, background): “see the series from Blom ASA (2011)), which might increase the ambiguity; (3) the increase in quantity of data sets with non-homogenous LODs (Fan et al., 2014; Touya and Reimer, 2015; Arroyo Ohori et al., 2015a); and (4) use cases have different requirements when it comes to the complexity and quali”
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets (2015, result): “The former has been found to be very high [1, 10, 13, 14, 15, 16, 28] when compared to proprietary mapping datasets, in a variety of countries across Europe.”
The world’s user-generated road map is more than 80% complete (2017, background): “More recently, studies have examined the quality of OSM data on building footprints [25], bicycle or pedestrian infrastructure [21, 26], points of interest [27], place names [28], and the classification of areal features [29].”