A Classification Model for the Inference of Spatial Precision of Openstreetmap Buildings With Intrinsic Indicators

Abstract

Abstract. To evaluate the quality of OSM data, similarities between OSM features and their homologous features represented in a reference database are relevant metrics. However, reference databases do not exist everywhere or are not freely available. Thus, having data quality assessment methods that rely only on intrinsic indicators (i.e. based on data itself without considering external information) would be useful in these cases. This article specifically uses the radial distance as a target quality metric to measure the quality of shapes. Its aim is to build a random-forest based classification method that reconstructs whether this distance is higher or lower than a specified threshold, using only intrinsic indicators as inputs. The classification algorithm is evaluated on a first dataset by computing the ROC (Receiver Operating Characteristic) curve and using the AUC (Area Under Curve) as an evaluation metric. The transferability of the resulting algorithm is then evaluated by measuring its performance on a second, distinct dataset. The experiments show that the algorithm performs reasonably well on both the initial and the second dataset, and that intrinsic indicators give relevant information to infer comparison-based shape quality (i.e. the radial distance).