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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,758 datasets
Approximately 0.0861 million kilometers of roads are mapped from OpenStreetMap for Kyrgyzstan, with AI-derived estimates classifying 9.7637% as paved and 14.5367% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
A dataset providing detailed road surface classification for Luxembourg, distinguishing paved and unpaved roads. It contains approximately 21.9 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 48.1% as paved and 20.2% as unpaved. The data is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated in March 2026.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for the Faroe Islands. It distinguishes paved and unpaved roads, covering approximately 0.0028 million kilometers of OpenStreetMap (OSM) data, with 25.71% paved and 5.7896% unpaved. The data is augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.
Approximately 242,100 kilometers of roads are mapped in OpenStreetMap for Ecuador, with AI-derived estimates classifying 41,500 km as paved and 59,200 km as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OSM data with deep learning predictions from Mapillary imagery and was last updated in March 2026. It is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Costa Rica. It covers approximately 68,000 km of OpenStreetMap roads, with deep learning predictions augmenting surface data for paved and unpaved segments. The data is derived from OSM, Mapillary imagery, and urban layers, and is available in GeoJSON and GeoPackage formats.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Mozambique. It covers approximately 360,300 km of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides detailed road surface information for Mali. It contains approximately 0.4783 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery to classify surfaces as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
Approximately 43,400 km of roads are mapped in OpenStreetMap for Liberia. A hybrid deep learning model classifies these roads as paved or unpaved, estimating 3.792% are paved and 37.3214% are unpaved, while 58.8866% of road surface information is missing in OSM. The dataset is produced by HeiGIT and was last updated in March 2026.
Approximately 1.3146 million kilometers of roads in Argentina are mapped, with AI-derived estimates classifying 15.4029% as paved and 48.8612% as unpaved. The dataset is created by HeiGIT, combining OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers, and was last updated in March 2026. It includes AI-predicted classes, OSM attributes, and urban classification features.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for Tuvalu. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery to label roads as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
HeiGIT's dataset provides detailed road surface information for Mauritania, distinguishing paved and unpaved roads. It contains approximately 0.0871 million km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 0.0123 million km as paved and 0.0433 million km as unpaved. The dataset was last updated on March 2, 2026.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Senegal. It covers approximately 243,600 kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data distinguishes paved and unpaved roads and is intended for transportation and infrastructure analysis.
Approximately 0.2232 million kilometers of roads in Denmark are mapped, with AI-derived estimates classifying 0.1015 million km as paved and 0.0388 million km as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated on March 2, 2026.
Approximately 226,400 km of Serbian roads are mapped, with AI-derived estimates classifying 18.6% as paved and 16.2% as unpaved. This dataset, created by HeiGIT and updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill information gaps. It includes attributes for highway type, surface, and urban classification.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Cuba. It covers approximately 130,200 kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning method. The data is augmented with predictions from Mapillary imagery and urban classifications from external layers.
HeiGIT's dataset provides AI-derived classifications for road surfaces in Tunisia, distinguishing paved from unpaved roads. It covers approximately 202,600 km of roads mapped in OpenStreetMap, with 11.75% classified as paved and 9.66% as unpaved. The data was last updated in March 2026 and combines OSM data with deep learning predictions from Mapillary imagery.
A geospatial dataset detailing road surfaces in Kiribati, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived surface classifications from Mapillary imagery and urban layers. It was last updated on March 2, 2026, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).
Falkland Islands (Malvinas) road network data provides AI-derived surface classifications for paved and unpaved roads. The dataset, created by HeiGIT, combines OpenStreetMap attributes with deep learning predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.
HeiGIT's dataset provides detailed road surface information for Qatar, distinguishing paved from unpaved roads. It contains approximately 0.0472 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 30.1549% as paved and 32.6687% as unpaved. The data was last updated on March 2, 2026, and is augmented with deep learning predictions from Mapillary imagery.
British Indian Ocean Territory road data provides AI-derived surface classifications for paved and unpaved roads, based on OpenStreetMap and Mapillary imagery. The dataset, created by HeiGIT and last updated in March 2026, includes approximately 0.0001 million kilometers of mapped roads. It is intended for transportation planning, infrastructure analysis, and GIS applications.