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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,731 datasets
Approximately 0.0001 million kilometers of roads are mapped in OpenStreetMap for Nauru. The dataset, created by HeiGIT and last updated in March 2026, classifies road surfaces as paved or unpaved using a hybrid deep learning approach augmented with Mapillary imagery. It includes attributes like highway type, surface, and urban classification.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Haiti. It covers approximately 52.5 thousand kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning approach. The data is augmented with urban classification layers and Mapillary imagery to address gaps in OSM surface tags.
A geospatial dataset detailing road surface types in Papua New Guinea, distinguishing paved from unpaved roads. It contains approximately 97,400 kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 7,600 km as paved and 29,700 km as unpaved. The dataset was created by HeiGIT and last updated in March 2026, combining OSM data with deep learning predictions from Mapillary imagery and urban classification layers.
Approximately 202,300 km of roads are mapped in OpenStreetMap for Burkina Faso, with AI-derived estimates classifying 6,400 km as paved and 70,500 km as unpaved. The dataset is produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026. It combines OSM data with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.
Bahamas road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset includes approximately 13,300 km of roads, with 19.7% classified as paved and 6.8% as unpaved. It was created by HeiGIT and last updated in March 2026.
Approximately 0.498 million kilometers of roads are mapped from OpenStreetMap for Greece, with AI-derived estimates classifying 18.85% as paved and 8.47% as unpaved. The dataset is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated in March 2026. It combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers.
HeiGIT's dataset provides AI-derived road surface classifications for Chile, distinguishing paved from unpaved roads. It covers approximately 0.4409 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery. The dataset was last updated on March 2, 2026.
HeiGIT's dataset provides detailed road surface information for El Salvador, distinguishing paved and unpaved roads. It combines OpenStreetMap data with AI-derived surface classifications from Mapillary imagery and urban layers. The dataset was last updated on March 2, 2026.
Algeria's road network, comprising approximately 459,200 kilometers of mapped roads, is classified by surface type using a hybrid deep learning approach. The dataset, created by HeiGIT and last updated in March 2026, augments OpenStreetMap data with AI predictions from Mapillary imagery and urban layers. It distinguishes paved from unpaved roads and quantifies gaps in surface information.
A geospatial dataset provides detailed information on road surfaces across Peru, distinguishing between paved and unpaved roads. It contains approximately 0.4981 million kilometers of roads mapped in OpenStreetMap, with surface classifications derived using a hybrid deep learning approach based on Mapillary imagery. The dataset was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and 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 the Republic of Moldova. It covers approximately 95,500 kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning model. The data is augmented with predictions from Mapillary imagery and urban classifications from GHSU and AFRICAPOLIS layers.
HeiGIT's dataset provides detailed road surface information for Guinea, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. It contains approximately 0.1897 million kilometers of mapped roads, with AI estimates classifying 2.6108% as paved and 27.2021% as unpaved. The dataset was last updated on March 2, 2026.
Approximately 0.0262 million kilometers of roads are mapped in OpenStreetMap for Eritrea. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach based on Mapillary imagery, augmented with urban classification layers. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
A 2026 dataset from HeiGIT (Heidelberg Institute for Geoinformation Technology) provides AI-derived road surface classifications for Australia. It covers approximately 1.83 million kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning approach. The data is augmented with predictions from Mapillary imagery and urban classification layers.
Approximately 471,300 km of roads are mapped in OpenStreetMap for the Republic of Korea, with AI-derived estimates classifying 75,100 km as paved and 31,800 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 urban layers. It was last updated on March 2, 2026.
HeiGIT's dataset provides AI-derived road surface classifications for the Dominican Republic, distinguishing paved from unpaved roads. It covers approximately 82,700 kilometers of roads mapped in OpenStreetMap, with 15% paved and 6.6% unpaved according to model predictions. The data was last updated on March 2, 2026.
HeiGIT provides AI-derived road surface classifications for South Georgia and the South Sandwich Islands, distinguishing paved from unpaved roads. The dataset combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS. It 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 Sudan. It covers approximately 501,200 kilometers of roads from OpenStreetMap, distinguishing between paved and unpaved surfaces. The data is augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.
A geospatial dataset provides detailed information on road surfaces in Botswana, distinguishing between paved and unpaved roads. It contains approximately 0.1982 million kilometers of roads mapped in OpenStreetMap, with surface classifications derived using a hybrid deep learning approach. The dataset was created by HeiGIT and last updated in March 2026.
Approximately 12,300 km of roads are mapped in OpenStreetMap for Timor-Leste. The dataset, created by HeiGIT, uses a hybrid deep learning approach to classify surfaces as paved or unpaved, augmenting OSM data with predictions from Mapillary imagery and urban layers. It was last updated in March 2026.