Chile GIS data

**UNOSAT code FR20221229CHL** This map illustrates satellite-detected burned areas in Viña del Mar Commune, Valparaíso Province, Valparaíso and Metropolitan Regions, Chile based on Sentinel-2 images acquired on 29 December 2022 at 11:52 local time. Within the map extent of about 1,700 ha, about 120 km² of lands appear to be potentially burned. Based on Worldpop population data and the burned area extent, about 4,000 people are potentially exposed or living close to burned areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Environment · United Nations Satellite Centre (UNOSAT)

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Wildfires in Valparaíso and Metropolitan Regions, Chile as of 29 December 2022

**UNOSAT code FR20221229CHL** This map illustrates satellite-detected burned areas in Valparaíso and Metropolitan Regions, Chile based on Sentinel-2 images acquired on 29 December 2022 at 11:52 local time. Within the analyzed area of about 3,400 km², about 90 km² of lands appear to be potentially burned. Based on Worldpop population data and the burned area extent, about 8,000 people are potentially exposed or living close to burned areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Environment · United Nations Satellite Centre (UNOSAT)

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Chile: WOF Administrative Subdivisions and Human Settlements

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.

Population · Who's On First

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Chile: Road Surface Data

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 Chile **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.4409 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.1117 million km (25.3428%) - Unpaved roads: 0.2348 million km (53.2609%) About 0.0943 million km (21.3964%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0011 million km (1.1864%) 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

Transport · HeiGIT (Heidelberg Institute for Geoinformation Technology)

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Chile - Spatial Distribution of Population

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Population · WorldPop (University of Southampton)

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The spatial distribution of population in 2024, Chile

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Population · WorldPop (University of Southampton)

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The spatial distribution of population in 2020 with country total adjusted to match the corresponding UNPD estimate, Chile

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