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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,758 datasets
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for Sao Tome and Principe. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery, covering approximately 1.7 km of roads. The data distinguishes paved and unpaved surfaces and is intended for transportation and infrastructure analysis.
A 2026 dataset from HeiGIT provides AI-derived road surface classifications for Togo, distinguishing paved and unpaved roads. It covers approximately 90,600 km of roads mapped in OpenStreetMap, with 6.3% paved and 38.4% unpaved according to model predictions. The data is augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.
HeiGIT's dataset provides AI-derived classifications of paved and unpaved roads in North Macedonia, based on OpenStreetMap data and a hybrid deep learning model. It covers approximately 34,400 km of mapped roads, with 16.8% classified as paved and 12.8% as unpaved. The dataset was last updated in March 2026 and is augmented with Mapillary imagery and urban classification layers.
HeiGIT's dataset provides AI-derived road surface classifications for Kazakhstan, distinguishing paved from unpaved roads. It covers approximately 0.5396 million kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The dataset was last updated on March 2, 2026.
Mongolia's road network, covering approximately 0.247 million kilometers mapped in OpenStreetMap, is classified by surface type. The dataset, created by HeiGIT and last updated in March 2026, uses a hybrid deep learning approach to distinguish paved from unpaved roads, augmenting OSM data with predictions from Mapillary imagery.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Bahrain. It covers approximately 14.8 thousand kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning approach. The data is augmented with predictions from Mapillary street-level imagery and urban layers from GHSU and AFRICAPOLIS.
Road surface data for Slovenia distinguishes paved and unpaved roads using a hybrid deep learning approach on OpenStreetMap and Mapillary imagery. The dataset covers approximately 106,100 kilometers of roads, with AI-derived estimates classifying 22.9% as paved and 16.3% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Approximately 1.2963 million kilometers of roads from OpenStreetMap for the United Kingdom, with AI-derived surface classifications for paved and unpaved segments. The dataset, created by HeiGIT and last updated in March 2026, combines OSM attributes with deep learning predictions from Mapillary imagery and urban classification layers.
HeiGIT's dataset provides detailed road surface information for Tonga, distinguishing paved and unpaved roads. It combines OpenStreetMap data with AI-derived classifications from Mapillary imagery and includes urban classification attributes. The dataset was last updated on March 2, 2026.
Approximately 0.3072 million kilometers of roads from OpenStreetMap in Bangladesh are classified by surface type using a hybrid deep learning approach. The dataset, created by HeiGIT and last updated in March 2026, distinguishes paved and unpaved roads and is augmented with predictions from Mapillary imagery and urban classification layers. It provides AI-derived estimates for 10.9845% paved and 5.798% unpaved roads, while noting 83.2175% of road surface information is missing in the original OSM data.
Approximately 51,500 km of roads in Iceland are mapped, with AI-derived estimates classifying 29.5% as paved and 40.3% as unpaved. This dataset from HeiGIT combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated on March 2, 2026.
Approximately 0.2248 million kilometers of roads are mapped for Bulgaria, with AI-derived estimates classifying 16.757% as paved and 7.3622% 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, and urban classification.
Road surface data for Bolivia distinguishing paved and unpaved roads, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset covers approximately 463,800 kilometers of roads, with AI-derived estimates classifying 6.4% as paved and 70.6% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
HeiGIT's dataset provides detailed road surface information for Guinea-Bissau, distinguishing paved from unpaved roads. It contains approximately 21,600 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 7.2% as paved and 38.1% as unpaved. The data was last updated on March 2, 2026, and combines OSM data with deep learning predictions from Mapillary imagery.
Approximately 70.1 thousand kilometers of roads are mapped in OpenStreetMap for Gabon, with AI-derived estimates classifying 4.6 thousand km as paved and 18.6 thousand km 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.
Approximately 0.1639 million kilometers of roads are mapped from OpenStreetMap for Lithuania, with AI-derived estimates classifying 22.4649% as paved and 29.5771% as unpaved. The dataset is produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) using a hybrid deep learning approach on Mapillary imagery and was last updated in March 2026. It includes OSM attributes, urban classifications, and AI prediction features to address gaps in surface information.
Tokelau's road network is classified by surface type using a hybrid deep learning approach applied to OpenStreetMap data. The dataset, created by HeiGIT and last updated in March 2026, includes AI-derived predictions for paved and unpaved roads. It integrates OSM attributes with urban classification layers and is intended to fill gaps in surface information.
HeiGIT's dataset provides AI-derived road surface classifications for Ukraine, distinguishing paved from unpaved roads. It covers approximately 1.188 million kilometers of roads mapped in OpenStreetMap, with 11.5% classified as paved and 11.3% as unpaved. The data was last updated on March 2, 2026.
Approximately 2.5 kilometers of roads are mapped in OpenStreetMap for Greenland. This dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), classifies these roads as paved or unpaved using a hybrid deep learning approach based on Mapillary imagery and urban layers. It was last updated on March 2, 2026.
Nigeria's road network, covering approximately 1.0944 million kilometers mapped in OpenStreetMap, is classified into paved and unpaved surfaces using a hybrid deep learning approach. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), augments OSM data with AI predictions from Mapillary imagery and urban layers, with the latest update in March 2026. It provides detailed attributes for each road segment, including predicted class, OSM tags, and urban classification.