Norway GIS data

geoBoundaries ADM1 boundaries for Norway: 11 units, representing 2022. Source: Kartverket.

**UNOSAT code FL20230811NOR** ette kartet viser kumulativt satellittoppdaget overflatevann langs med Gudbrandsdalslågen i Ringebu, Sør-Fron og Nord-Fron kommune, Innlandet fylke, Norge, observert fra et Sentinel-1 bilde tatt den 10. august 2023 kl. 17:03 UTC. Innenfor det analyserte området på omtrent 500 km², er omtrent 15 km² med land observert som oversvømt. Basert på befolkningsdata fra Worldpop og det kartlagte overflatevannet er omtrent 800 personer potensielt eksponert eller bor i nærheten av det oversvømte områder. Dette er en foreløpig analyse og er ennå ikke validert på feltet. Vennligst send tilbakemelding fra bakken til FNs satellittsenter (UNOSAT).

**UNOSAT code FL20230811NOR** Live web map showing analysis related to floods in Norway

**UNOSAT code FL20230811NOR** This map illustrates satellite-detected water extent as observed from a Pléiades imagery acquired on the 12 August 2023 at 13:31 local time.Within the analysed area of about 16,000 ha, a total of about 255 ha of lands appear to be affected with flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between paved and unpaved roads across the region. Surface information is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name: "Norway: **Planet** Road Surface". The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 1.9661 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.6942 million km (35.3065%) - Unpaved roads: 0.2998 million km (15.2463%) About 0.9908 million km (50.3928%) of roads lack surface information in OSM. To reduce this gap, the Mapillary-derived surface dataset provides an additional 0.0157 million km (1.5842%) of surface information. Intended Use This dataset supports transportation planning, infrastructure assessment, climate emissions analysis, and GIS applications. AI Features - pred_class: Model-predicted surface class ("paved" or "unpaved") - pred_label: Binary label (0 = paved, 1 = unpaved) - osm_surface_class: Surface classification derived from OSM tags - combined_surface_osm_priority: Combined classification prioritizing OSM - combined_surface_DL_priority: Combined classification prioritizing DL prediction - n_of_predictions_used: Number of predictions used for estimation - predicted_length: DL-estimated length (meters) - DL_mean_timestamp: Mean timestamp of predictions OSM Attributes - name - name:en - name:* (local language) - highway - surface - smoothness - width - lanes - oneway - bridge - layer - source Urban Classification Attributes - continent - country_iso_a2 - urban (0 = rural, 1 = urban) - urban_area - osm_id - osm_type Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

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Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT