ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 39–47, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-39-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 39–47, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-39-2020

  03 Aug 2020

03 Aug 2020

A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS

I. Maidaneh Abdi1,2, A. Le Guilcher1, and A-M. Olteanu-Raimond1 I. Maidaneh Abdi et al.
  • 1Univ. Paris-Est, LASTIG MEIG, IGN, ENSG, F-94160 Saint-Mandé, France
  • 2ITU-I, Djibouti University , Djibouti

Keywords: Regression, Intrinsic and Extrinsic Quality, Spatial Accuracy, OpenStreetMap, Reference Data, Multi-criteria Data Matching, Spatial Data, Belief Theory

Abstract. Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor.