Portugal GIS data
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Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Portugal Postcodes1 Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Informação geográfica cedida pela Direção-Geral do Território. These were published in March 2024. Other administrative boundaries for this country are also available: Country Distrito Postcodes2 Concelho Postcodes4 Freguesia CensusSection CensusSubsection
aircraft imagery by Pedroso_Rego, acquired 2025-09-05 to 2025-09-06, 5.0 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM3 boundaries for Portugal: 2905 units, representing 2021. Source: geoBoundaries, DG Territory.
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Portugal Census Section Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Informação geográfica cedida pela Direção-Geral do Território. These were published in March 2024. Other administrative boundaries for this country are also available: Country Postcodes1 Distrito Postcodes2 Concelho Postcodes4 Freguesia CensusSubsection
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Portugal Postcodes2 Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Informação geográfica cedida pela Direção-Geral do Território. These were published in March 2024. Other administrative boundaries for this country are also available: Country Postcodes1 Distrito Concelho Postcodes4 Freguesia CensusSection CensusSubsection
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Portugal Postcodes4 Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Informação geográfica cedida pela Direção-Geral do Território. These were published in March 2024. Other administrative boundaries for this country are also available: Country Postcodes1 Distrito Postcodes2 Concelho Freguesia CensusSection CensusSubsection
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 classification 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 Portugal **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.4063 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0858 million km (21.1121%) - Unpaved roads: 0.0428 million km (10.5392%) About 0.2777 million km (68.3487%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0032 million km (1.1438%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, 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**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used 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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The INFOSOLO legacy database is the first effort to develop a soil information system in Portugal, suitable to compile soil data produced in the country, and to support stakeholders and land managers in decision-making. The current version includes soil data from a set of 9934 horizons/layers studied in 3461 soil profiles across the country between 1966 and 2014. Data was extracted from scattered soil surveys, research projects, and academic studies carried out by public Portuguese and other European institutions. As the level of detail of soil information varied between datasets, a series of validation tests and harmonization procedures were implemented in order to access and improve the quality of the data