Inferring the Scale of OpenStreetMap Features

Touya, Guillaume; Reimer, Andreas W.

doi:10.1007/978-3-319-14280-7_5

Cited by 29 publications

(28 citation statements)

References 27 publications

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“…These findings indicate that the scale in this study area seems to have minor influence on the results. Nevertheless, further research is required to investigate the influence of scale in this context, as for example Touya and Reimer [35] already did for individual objects. To investigate a possible link between urbanization and OSM quality, the relation of population density and OSM coverage, as well as population density and kappa were visualized ( Figure 5).…”

Section: Resultsmentioning

confidence: 99%

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Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany

Dorn

Törnros

Zipf

2015

IJGI

119

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Volunteered Geographic Information (VGI) such as data derived from the OpenStreetMap (OSM) project is a popular data source for freely available geographic data. Normally, untrained contributors gather these data. This fact is frequently a cause of concern regarding the quality and usability of such data. In this study, the quality of OSM land use and land cover (LULC) data is investigated for an area in southern Germany. Two spatial data quality elements, thematic accuracy and completeness are addressed by comparing the OSM data with an authoritative German reference dataset. The results show that the kappa value indicates a substantial agreement between the OSM and the authoritative dataset. Nonetheless, for our study region, there are clear variations between the LULC classes. Forest covers a large area and shows both a high OSM completeness (97.6%) and correctness (95.1%). In contrast, farmland also covers a large area, but for this class OSM shows a low completeness value (45.9%) due to unmapped areas. Additionally, the results indicate that a high population density, as present in urbanized areas, seems to denote a higher strength of agreement between OSM and the DLM (Digital Landscape Model). However, a low population density does not necessarily imply a low strength of agreement.

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Section: Resultsmentioning

confidence: 99%

“…However, the DLM has a fixed scale, while in the OpenStreetMap natural dataset the level of detail and consequently the scale varies, as shown by the study of Touya and Reimer [35]. Both datasets are from 2014, OSM was downloaded in March and the Base DLM was obtained in May.…”

Section: Study Area and Datamentioning

confidence: 99%

Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany

Dorn

Törnros

Zipf

2015

IJGI

119

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“…To be more specific, a comparison between them in terms of MMU, linear width, nomenclature richness (i.e., number of classes), temporal coverage and update period, spatial coverage (globally and in Germany), and positional accuracy is detailed. From an MMU viewpoint, ATKIS and CORINE account for the best and worst examples for capturing MMU and positional accuracy; however, OSM also provides very fine geometric information as outlined in Touya & Reimer (2015). GlobeLand30 is potentially a better data source of land cover information than CORINE in terms of MMU and positional accuracy.…”

Section: Technical Comparisons Between the Existing Datasetsmentioning

confidence: 99%

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani

See

Tayyebi

2016

International Journal of Digital Earth

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Global land cover maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection and environmental planning. Due to the importance of land cover, there is a pressing need to increase the temporal and spatial resolution of global land cover maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, i.e. CORINE, Urban Atlas, OpenStreetMap and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing land cover in Germany, and paves the way for further regional and national validation efforts.

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“…The heterogeneity of the collection affects the scale of the OSM geodatabase [65]. Moreover, the LoD varies not only from one theme layer to another (e.g., the buildings are detected with different detail than the road edges), but also from one feature to another in the same theme [51].…”

Section: Methodology: Spatial Accuracymentioning

confidence: 99%

A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints

Brovelli

Zamboni

2018

IJGI

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OpenStreetMap (OSM) is currently the largest openly licensed collection of geospatial data, widely used in many projects as an alternative to or integrated with authoritative data. One of the main criticisms against this dataset is that, being a collaborative product created mainly by citizens without formal qualifications, its quality has not been assessed and therefore its usage can be questioned for some applications. This paper provides a map matching method to check the spatial accuracy of the building footprint layer, based on a comparison with a reference dataset. Moreover, from the map matching and a similarity check, buildings can be detected and therefore an index of completeness can also be computed. This process has been applied in Lombardy, a region in Northern Italy, covering an area of 23,900 km 2 and comprising respectively about 1 million buildings in OSM and 2.8 million buildings in the authoritative dataset. The results of the comparison show that the positional accuracy of the OSM buildings is at least compatible with the quality of the reference dataset at the scale of 1:5000 since the average deviation, with respect to the authoritative map, is below the expected tolerance of 3 m. The analysis of completeness, given in terms of the number of buildings appearing in the authoritative dataset and not present in OSM, shows an average percentage in the whole region equal to 57%. However, worth noting that the opposite, namely the number of buildings in OSM and not in the reference dataset, is not zero, but corresponds to 9%. The OSM building map can therefore be considered to be a valid base map for direct use (territorial frameworks, map navigation, urban analysis, etc.) and for derived use (background for the production of thematic maps) in all those cases where an accuracy corresponding to 1:5000 is required. Moreover it could be used for integrating the authoritative map at this scale (or smaller) where it is not complete and a rigorous quality certification in terms of metric precision is not required.

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Inferring the Scale of OpenStreetMap Features

Cited by 29 publications

References 27 publications

Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany

Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints

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