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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,753 datasets
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Barbados. It contains approximately 4.7 kilometers of roads from OpenStreetMap, with deep learning predictions augmenting surface tags. The data is intended for transportation planning, infrastructure analysis, and GIS applications.
OpenStreetMap road data for Jamaica, augmented with deep learning predictions from Mapillary imagery. The dataset includes approximately 0.0295 million km of roads, with AI-derived estimates classifying 26.9897% as paved and 5.1163% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Egypt's road network, covering approximately 0.6438 million kilometers, is classified into paved and unpaved surfaces. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.
Zimbabwe road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset covers approximately 294,500 km of roads, with deep learning predictions for paved and unpaved surfaces. It was created by HeiGIT and last updated in March 2026.
Cook Islands road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset includes approximately 0.0006 million kilometers of roads, with classifications for paved and unpaved surfaces. It was created by HeiGIT and last updated in March 2026.
A dataset providing detailed information on road surfaces in Guyana, distinguishing between paved and unpaved roads. It contains approximately 0.0157 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 8.1637% as paved and 25.1713% as unpaved. The data was created by HeiGIT and last updated on March 2,ζ们εη°δΊδΈδΈͺιθ――γ
Approximately 27.6 km of roads are mapped in OpenStreetMap for Bhutan. The dataset, created by HeiGIT, classifies road surfaces as paved or unpaved using a hybrid deep learning approach on Mapillary imagery and was last updated in March 2026. It includes AI-derived predictions and OSM attributes, with deep learning estimates covering about 11.2% of the mapped road network.
Approximately 18,300 kilometers of roads are mapped in OpenStreetMap for Fiji. 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 created by HeiGIT (Heidelberg Institute for Geoinformation Technology) and last updated in March 2026.
Approximately 0.017 million km of roads are mapped in OpenStreetMap for the Solomon Islands. This dataset, created by HeiGIT and last updated in March 2026, classifies road surfaces as paved or unpaved using a hybrid deep learning approach on Mapillary imagery, augmented with urban layers. It provides AI-derived estimates for paved, unpaved, and missing surface information.
Approximately 0.3295 million kilometers of roads in Hungary are classified as paved or unpaved using a hybrid deep learning approach. The dataset, created by HeiGIT, combines OpenStreetMap data with predictions from Mapillary imagery and urban layers to address a 65.2% surface information gap in OSM. It was last updated on March 2, 2026.
0.0022 million kilometers of roads from OpenStreetMap are classified as paved or unpaved using a hybrid deep learning approach. The dataset, created by HeiGIT, was last updated on March 2, 2026, and integrates Mapillary imagery with urban layers from GHSU and AFRICAPOLIS. It estimates that 9.0246% of mapped roads are paved and 12.811% are unpaved, while 78.1644% of surface information is missing in OSM.
HeiGIT's dataset provides detailed road surface information for Malaysia, classifying roads as paved or unpaved. It contains approximately 0.4788 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates covering 15.9342% paved and 13.4757% unpaved. The data was last updated on March 2, 2026, and combines OSM data with deep learning predictions from Mapillary imagery.
Saint Vincent and the Grenadines road network data distinguishes paved and unpaved surfaces using a hybrid deep learning approach on OpenStreetMap and Mapillary imagery. The dataset includes approximately 0.9 km of mapped roads, with AI-derived estimates classifying 14.7% as paved and 5.8% as unpaved. It was created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Approximately 1.8582 million kilometers of roads in Indonesia are mapped, with AI-derived surface classifications for paved and unpaved segments. This dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It was last updated in March 2026.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Brazil. It covers approximately 4.57 million kilometers of OpenStreetMap roads, distinguishing paved and unpaved surfaces using a hybrid deep learning model. The data is augmented with urban classification layers and Mapillary imagery to address missing surface information.
Approximately 0.0484 million kilometers of roads are mapped in OpenStreetMap for Cyprus. The dataset, created by HeiGIT, uses a hybrid deep learning approach to classify roads as paved or unpaved, augmenting OSM data with predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.
Approximately 0.7 km of roads are mapped in OpenStreetMap for the British Virgin Islands. The dataset, created by HeiGIT, classifies road surfaces as paved or unpaved using a hybrid deep learning approach based on Mapillary imagery and OSM data. It was last updated in March 2026.
Vietnam's road network is detailed in this dataset, which contains approximately 0.8443 million kilometers of roads from OpenStreetMap. The Heidelberg Institute for Geoinformation Technology (HeiGIT) augmented this data using a hybrid deep learning approach on Mapillary imagery to classify surfaces as paved or unpaved, with the dataset last updated in March 2026.
Bosnia and Herzegovina road network data from OpenStreetMap, augmented with AI-derived surface classifications. The dataset contains approximately 0.1119 million kilometers of roads, with deep learning predictions distinguishing paved and unpaved surfaces. It was created by HeiGIT and last updated in March 2026.
HeiGIT's Samoa: Road Surface Data provides detailed information on road surfaces across Samoa, distinguishing between paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, combined with urban classification layers. It was last updated on March 2, 2026.