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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,731 datasets
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.
Approximately 0.29 million kilometers of roads are mapped in OpenStreetMap for Nepal. 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.
Geoscience Australia's marine survey S282 detected a variety of organic contaminants in sediment samples, including fatty acid amides, chemical antioxidants, and UV absorbers. The dataset documents the identification of contamination sources, such as specific plastic sampling bags and sunscreen, which could be mistaken for natural petroleum evidence. This work aims to inform sample handling protocols for organic geochemists involved in multi-disciplinary studies.
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.