A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS
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 s...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-08-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2020/39/2020/isprs-annals-V-4-2020-39-2020.pdf |
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| author | I. Maidaneh Abdi I. Maidaneh Abdi A. Le Guilcher A.-M. Olteanu-Raimond |
| author_facet | I. Maidaneh Abdi I. Maidaneh Abdi A. Le Guilcher A.-M. Olteanu-Raimond |
| author_sort | I. Maidaneh Abdi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T20:54:58Z |
| format | Article |
| id | doaj.art-7f6d595e7b20403288e92f9548e91d9d |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-10T20:54:58Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-7f6d595e7b20403288e92f9548e91d9d2022-12-22T01:33:59ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502020-08-01V-4-2020394710.5194/isprs-annals-V-4-2020-39-2020A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGSI. Maidaneh Abdi0I. Maidaneh Abdi1A. Le Guilcher2A.-M. Olteanu-Raimond3Univ. Paris-Est, LASTIG MEIG, IGN, ENSG, F-94160 Saint-Mandé, FranceITU-I, Djibouti University , DjiboutiUniv. Paris-Est, LASTIG MEIG, IGN, ENSG, F-94160 Saint-Mandé, FranceUniv. Paris-Est, LASTIG MEIG, IGN, ENSG, F-94160 Saint-Mandé, FranceData 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.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2020/39/2020/isprs-annals-V-4-2020-39-2020.pdf |
| spellingShingle | I. Maidaneh Abdi I. Maidaneh Abdi A. Le Guilcher A.-M. Olteanu-Raimond A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS |
| title_full | A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS |
| title_fullStr | A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS |
| title_full_unstemmed | A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS |
| title_short | A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS |
| title_sort | regression model of spatial accuracy prediction for openstreetmap buildings |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2020/39/2020/isprs-annals-V-4-2020-39-2020.pdf |
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