OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3 in both standard formats (OSM PBF, XML) and cloud-native formats optimized for analytics workloads.

Daily extract of buildings in metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the carto portal

— analysable over the last 30 days via the Change Tracking Portal

Data model The OSM attributes used to filter the data are: * building=*

Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about: * the building-specific data model on the OSM Wiki page — FR:Key:building=* [2]

• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].
• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:Key:building [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques

[4] https://taginfo.openstreetmap.org/keys/building

Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyrightDaily extract of buildings in metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the carto portal

— analysable over the last 30 days via the Change Tracking Portal

Data model

The OSM attributes used to filter the data are:

• building=*

Additional OSM attributes have been selected to enrich the main tags.

All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about:

• the building-specific data model on the OSM Wiki page — FR:Key:building=* [2]

• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].

• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale

[2] https://wiki.openstreetmap.org/wiki/FR:Key:building

[3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques

[4] https://taginfo.openstreetmap.org/keys/building

Credits

© OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

Full video tutorial on how to create this model can be found here: https://youtu.be/4HBhnh7U7L0

• Generator: DEM Net Elevation API
• Digital Elevation Model: AW3D30 OpenTopography
• Imagery: MapBox Satellite
• Data: OpenStreetMap

Source: Objaverse 1.0 / Sketchfab

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.0497 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0117 and 0.0111 (in million kms), corressponding to 23.4856% and 22.3095% respectively of the total road length in the dataset region. 0.0269 million km or 54.2049% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0008 million km of information (corressponding to 3.0011% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

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 meters 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\%, which was mainly influenced by the uncertainty of the public accessibility model.

limited access to high-resolution

OpenStreetMap Dataset

This is a parsed and enriched dataset of all OpenStreetMap changesets, changeset comments, notes, and note comments. The dataset includes 4 different datasets:

changeset_data - All OpenStreetMap changesets with enriched metadata (partitioned by year and month) changeset_comments_data - Comments on changesets from changeset discussions notes_data - Notes on the map with their locations and status notes_comments_data - Comments and actions on notes

This dataset… See the full description on the dataset page: https://huggingface.co/datasets/piebro/osm-data.

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for French Polynesia in a GIS-friendly format.

The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly.

The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

OpenStreetMap data is open data, with a very permissive licence. You can download it and use it for any purpose you like, as long as you credit OpenStreetMap and its contributors. You don't have to pay anyone, or ask anyone's permission. When you download and use the data, you're granted permission to do that under the Open Database Licence (ODbL). The only conditions are that you Attribute, Share-Alike, and Keep open.

The required credit is “© OpenStreetMap contributors”. If you make a map, you should display this credit somewhere. If you provide the data to someone else, you should make sure the license accompanies the data

Daily extract of Copper Subdistributors (SR) in Metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1]. The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the carto portal — analysable over the last 30 days via the Change Tracking Portal Data model The OSM attributes used to filter the data are: * Telecom =connection_point * telecom:medium=copper We find in the layer all the objects concerned whether they are mapped in the form of a node, path or relationship in OSM. For polygons and multi-polygons, the geometry provided corresponds to the centroid, the original geometry is available in EWKT format via the attribute ‘osm_original_geom’ and the original type in a column ‘osm_type’. Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object. More information about: * the data model specific to ‘connection points’ on the Wiki page OSM — FR:Tag:telecom=connection_point [2] * the general OSM data model is documented on the OSM Wiki page — Map Elements [3]. * the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4] [1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:Tag:telecom=connection_point [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.org/tags/?key=telecom&value=connection_point#overview Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for 21 Pacific Island Countries, in a GIS-friendly format. The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly and contains data for Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Republic of the Marshall Islands, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Guam, Northern Mariana Islands, French Polynesia, Wallis and Futuna, Tokelau, American Samoa as well as data on the Pacific region. The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

Daily extract of the cycle network (infrastructure) in metropolitan France in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the portal — analysable over the last 30 days via the Change Tracking Portal

Data model The OSM attributes used to filter the data are:

• highway=cycleway
• Bicycle=designated
• cycleway=* (except ‘no’)
• cycleway:left=* (except ‘no’)
• cycleway:right=* (except ‘no’)
• cycleway:both=* (except ‘no’)

Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about:

• the cycle network-specific data model on the Wiki page OSM — FR:Bicycle [2].
• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].
• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:Bicycle [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.fr/search?q=bycicle

Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

Daily extract of the bus routes in metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the portal — analysable over the last 30 days via the Change Tracking Portal

Data model The OSM attributes used to filter the data are:

• route = bus

Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about:

• the bus route-specific data model on the OSM Wiki page — Tag:route=bus [2].
• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].
• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/Tag:route%3Dbus [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.fr

Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

Daily extract of hospitals in metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the carto portal — analysable over the last 30 days via the Change Tracking Portal

Data model The OSM attributes used to filter the data are:

• amenity=hospital

We find in the layer all the objects concerned whether they are mapped in the form of a node, path or relationship in OSM. For polygons and multi-polygons, the geometry provided corresponds to the centroid, the original geometry is available in EWKT format via the attribute ‘osm_original_geom’ and the original type in a column ‘osm_type’.

Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about:

• the ** hospital-specific data model** on the OSM Wiki page — EN:Tag:amenity=hospital [2]
• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].
• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:Key:amenity [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.fr

Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

Daily extract from the road network (infrastructure) in metropolitan France in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the portal — analysable over the last 30 days via the Change Tracking Portal

Data model The OSM attributes used to filter the data are:

• highway = motorway
• highway = trunk
• highway = primary
• highway = secondary
• highway = tertiary
• highway = motorway_link/trunk_link/primary_link/secondary_link/tertiary_link/motorway_junction
• highway = unclassified
• highway = residential
• highway = service
• highway = pedestrian
• highway = living_street
• highway = track

Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about:

• the road network data model on the Wiki page OSM — FR:France roads tagging [2].
• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].
• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:France_roads_tagging [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.fr/keys/highway

Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

Daily extract of the railways (infrastructure) in metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1].

The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the portal — analysable over the last 30 days via the Change Tracking Portal

Data model The OSM attributes used to filter the data are:

• railway = rail
• railway = narrow_gauge

Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object.

More information about:

• the railway-specific data model on the Wiki page OSM — FR:Chemins de fer [2].
• the general OSM data model is documented on the OSM Wiki page — Map Elements [3].
• the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4]

[1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:Chemins_de_fer [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.fr/keys/railway

Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright

This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) buildings data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

Overview: osm: Military rasterized from OSM landuse polygons, first to 10m spatial resolution and after downsampled to 30m by spatial average.

Traceability (lineage): The class-wise layers of this dataset were extracted from OpenStreetMap data downloaded from geofabrik.de and aggregated based on labels assigned to the volunteered geographical information objects.

Scientific methodology: nan

Usability: The extracted classes can be used to preprocess training data (as detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). Users are advised to remember the potential inconsistencies in volunteered geographical information, however: Some regions of Europe have been less consistently mapped in OpenStreetMap. This may introduce bias in any subsequent modelling.

Uncertainty quantification: nan

Data validation approaches: This dataset has not been validated

Completeness: Volunteered geographical information often more complete in regions with more active contributors. It is likely that this dataset contains many omission errors in regions of Europe where OpenStreetMap is used less intensively.

Consistency: Volunteered geographical information often more complete in regions with more active contributors. It is likely that this dataset contains many omission errors in regions of Europe where OpenStreetMap is used less intensively.

Positional accuracy: The rasters have a spatial resolution of 30m

Temporal accuracy: The maps are based on an extract from 2020.

Thematic accuracy: The 30m pixels of each OSM extract map have values ranging from 0-100, indicating the density aggregated from 10m pixels where rasterized objects burned the value 100 in a 0-value raster.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

This web map features a vector basemap of OpenStreetMap (OSM) data created and hosted by Esri. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, and rendered using a creative cartographic style emulating a blueprint technical drawing. The vector tiles are updated every few weeks with the latest OSM data. This vector basemap is freely available for any user or developer to build into their web map or web mapping apps.OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

This dataset contains tile imagery from the OpenStreetMap project alongside label masks for buildings from OpenStreetMap. Besides the original clean label set, additional noisy label sets for random noise, removed and added buildings are provided.

The purpose of this dataset is to provide training data for analysing the impact of noisy labels on the performance of models for semantic segmentation in Earth observation.

The code for downloading and creating the datasets as well as for performing some preliminary analyses is also provided, however it is necessary to have access to a tile server where OpenStreetMap tiles can be downloaded in sufficient amounts.

To reproduce the dataset and perform analysis on it, do the following:

• unzip data.zip and code.zip
• create the folder structure from data
• Build and activate a python environment from environment.yml
• Insert the url of a suitable tile server for OSM tiles in line 76 of utils.py
• Execute download_OSM_dataset.py to download OSM image tiles alongside OSM labels
• Execute create_noisy_labels.py for the OSM dataset to create noisy label sets
• Divide the images and labels into train and test data. split_data.py can be used as a baseline for this, but pathnames have to be adjusted and the corresponding directories have to be created first.
• Call train_model.py to train a model on the data. Specify the data size and the label set by giving command line arguments as shown in train_model.sh