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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,739 datasets
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Ghana. It covers approximately 204,300 kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The dataset distinguishes paved and unpaved roads and is intended for transportation and infrastructure analysis.
Approximately 1.9 km of roads are mapped in OpenStreetMap for Andorra, with AI-derived estimates classifying 19.33% as paved and 11.14% as unpaved. This dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated in March 2026.
Turkmenistan's road network is classified by surface type using a hybrid deep learning approach on OpenStreetMap data. The dataset covers approximately 86,800 km of roads, with AI-derived estimates classifying 24.4% as paved and 14.9% as unpaved. It was created by HeiGIT and last updated in March 2026.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Taiwan (Province of China). It covers approximately 0.2292 million kilometers of OpenStreetMap roads, distinguishing paved and unpaved surfaces using a hybrid deep learning approach on Mapillary imagery. The data is augmented with urban classification layers and is intended for transportation and infrastructure analysis.
A geospatial dataset detailing road surfaces in Liechtenstein, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived classifications using a hybrid deep learning approach based on Mapillary imagery. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Detailed information on road surfaces in Lesotho, distinguishing between paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It was last updated on March 2, 2026.
A dataset from 2026 provides detailed road surface information for Somalia, distinguishing between paved and unpaved roads. It contains approximately 0.4947 million kilometers of roads mapped in OpenStreetMap, augmented with deep learning predictions from Mapillary imagery. The data was created by HeiGIT (Heidelberg Institute for Geoinformation Technology) and is available in GEOJSON and GEOPACKAGE formats.
Approximately 0.0017 million kilometers of roads are mapped for Dominica, with 23.2719% classified as paved and 7.7348% as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), uses a hybrid deep learning approach on OpenStreetMap and Mapillary imagery, augmented with urban classification layers. It was last updated on March 2, 2026.
Iraq road surface data distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap (OSM) and Mapillary imagery. Approximately 0.2839 million kilometers of roads are mapped, with AI-derived estimates for 9.1664% paved and 3.1261% unpaved. The dataset was produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Approximately 0.3 km of roads are mapped for Montserrat, with 26.5% classified as paved and 10.7% as unpaved. The dataset is produced by HeiGIT using a hybrid deep learning approach on Mapillary imagery and OSM data, combined with urban layers. It was last updated on March 2, 2026.
Approximately 0.6328 million kilometers of roads in Finland are mapped, with AI-derived estimates classifying 16.9897% as paved and 40.083% as unpaved. The dataset is produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill gaps in surface information.
HeiGIT's dataset provides detailed road surface classifications for Israel, distinguishing paved and unpaved roads. It combines OpenStreetMap data with AI-derived predictions from Mapillary imagery, covering approximately 0.1301 million kilometers of roads. The dataset was last updated in March 2026.
Approximately 0.4963 million kilometers of Philippine roads are mapped, with AI-derived estimates classifying 16.4302% as paved and 10.3289% as unpaved. The dataset, created by HeiGIT and last 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 condition, and urban classification.
HeiGIT's dataset provides detailed road surface classifications for Tanzania, distinguishing paved and unpaved roads. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery, covering approximately 0.9904 million kilometers of roads. The dataset was last updated on March 2, 2026.
Singapore's road network, with approximately 44,300 km of roads mapped in OpenStreetMap. The dataset, created by HeiGIT, classifies road surfaces as paved or unpaved using a hybrid deep learning approach on Mapillary imagery, augmented with urban classification layers. It was last updated on March 2, 2026.
Approximately 68,800 km of roads are mapped in OpenStreetMap for Sierra Leone, with AI-derived estimates classifying 4.7% as paved and 31.6% 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.
Road surface data for Switzerland distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap (OSM) and Mapillary imagery. The dataset covers approximately 0.2896 million kilometers of roads, with AI-derived estimates classifying 28.4811% as paved and 15.0571% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026.
HeiGIT provides a dataset detailing road surfaces in Sri Lanka, distinguishing paved and unpaved roads. It covers approximately 163,700 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 17.2% as paved and 11.8% as unpaved. The dataset was last updated on March 2, 2026.
A geospatial dataset providing detailed information on road surfaces across China, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived surface classifications from Mapillary imagery and PlanetScope satellite data. It was created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026.
Zambia's road network is detailed in this dataset, which distinguishes paved and unpaved roads. It contains approximately 0.3549 million kilometers of roads mapped in OpenStreetMap, with AI-derived surface classifications for about 33% of them. The data was created by HeiGIT and last updated in March 2026.