OpenStreetMap in GIScience

Arsanjani, Jamal Jokar; Zipf, Alexander; Mooney, Peter; Helbich, Marco

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

Cited by 82 publications

(4 citation statements)

References 141 publications

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“…Feltynowski et al [14] and Le Texier et al [15] extracted urban green spaces from OSM using a list of green space related OSM tags based on expert knowledge. These studies concluded that the public urban green spaces in OSM resembled quite well the ones mapped in authoritative data sets, especially in city centers where data quality is likely to be higher [52]. Other studies such as Fonte et al [53] and Arsanjani et al [52] mapped OSM tags to the nomenclatures of the Urban Atlas and CORINE Land Cover to derive urban land use maps which included artificial non-agricultural vegetated areas and urban green spaces.…”

Section: Related Workmentioning

confidence: 89%

“…These studies concluded that the public urban green spaces in OSM resembled quite well the ones mapped in authoritative data sets, especially in city centers where data quality is likely to be higher [52]. Other studies such as Fonte et al [53] and Arsanjani et al [52] mapped OSM tags to the nomenclatures of the Urban Atlas and CORINE Land Cover to derive urban land use maps which included artificial non-agricultural vegetated areas and urban green spaces. Methods which combine OSM and remote sensing data have not been proposed specifically for urban green space mapping, but for land use mapping in general.…”

Section: Related Workmentioning

confidence: 89%

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Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Ludwig

Hecht

Lautenbach

et al. 2021

IJGI

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Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

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Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 89%

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Ludwig

Hecht

Lautenbach

et al. 2021

IJGI

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“…We used the OSM data for all building footprints in Brandenburg from September 2019 to assess the degree of urbanisation in each hexagon. OSM is an open-source, crowd-sourced mapping platform that has high coverage and good quality in countries such as Germany (Fan et al 2014;Jokar Arsanjani et al 2015). We used April 2019 settlement data from the Landesentwicklungsplan Hauptstadtregion Berlin Brandenburg for calculating the mean Euclidean distance to settlements for each cell.…”

Section: Datamentioning

confidence: 99%

Agricultural Landscapes in Brandenburg, Germany: An Analysis of Characteristics and Spatial Patterns

et al. 2021

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The increasing demand for agricultural commodities for food and energy purposes has led to intensified agricultural land management, along with the homogenization of landscapes, adverse biodiversity effects and robustness of landscapes regarding the provision of ecosystem services. At the same time, subsidized organic agriculture and extensive grassland use supports the provision of ecosystem services. Yet little is understood about how to evaluate a landscape’s potential to contribute to protecting and enhancing biodiversity and ecosystem services. To address this gap, we use plot-level data from the Integrated Administration and Control System (IACS) for Germany’s federal state of Brandenburg, and based on a two-step cluster analysis, we identify six types of agricultural landscapes. These clusters differ in landscape structure, diversity and measures for agricultural land management intensity. Agricultural land in Brandenburg is dominated by high shares of cropland but fragmented differently. Lands under organic management and those with a high share of maize show strong spatial autocorrelation, pointing to local clusters. Identification of different types of landscapes permits locally- and region-adapted designs of environmental and agricultural policy measures improves outcome-oriented environmental policy impact evaluation and landscape planning. Our approach allows transferability to other EU regions.

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“…The most well-known VGI realisation is probably OpenStreetMap (OSM- Arsanjani et al 2015). In many areas, the OSM data coverage for certain data classes even exceeds the one provided by the authoritative topographic database (Haklay 2010).…”

Section: Introductionmentioning

confidence: 99%

Map Gretel: social map service supporting a national mapping agency in data collection

2018

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This study presents results from an ongoing social map service pilot that supports a national mapping agency (NMA) in data collection. Results from the pilot show that a VGI map service benefits both citizens and the NMA; the data quality is high enough to suit the needs of an NMA; citizens obtain a means to contribute and be involved in ameliorating maps. The social map service works also as a citizen-tocitizen communication channel as all the contributions are immediately visible to all users. Based on the results of this study, VGI should be further integrated with NMA processes in the future. One way of improving the integration are via services offered to both citizens and NMA employees that borrow features from existing social network services but also from popular games. Commenting, voting and gamification elements can be integrated with the VGI services to benefit both citizens and NMAs. Due to the numerous challenges of VGI the European national mapping agencies involvement in using citizens as data collectors is low. It is, however, within the NMA's grasp to develop the role of VGI in NMA processes further and to make VGI collection fun.

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OpenStreetMap in GIScience

Cited by 82 publications

References 141 publications

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Agricultural Landscapes in Brandenburg, Germany: An Analysis of Characteristics and Spatial Patterns

Map Gretel: social map service supporting a national mapping agency in data collection

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