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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,731 datasets
HeiGIT provides a geospatial dataset detailing road surfaces in Montenegro, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset covers approximately 31.5 km of roads, with AI estimates classifying 19.2% as paved and 11.9% as unpaved. It was last updated on March 2, 2026.
South Sudan road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset covers approximately 0.1612 million kilometers of roads, distinguishing paved and unpaved surfaces using a hybrid deep learning approach based on Mapillary imagery. It was created by HeiGIT and last updated in March 2026.
Approximately 0.1506 million kilometers of roads are mapped in this dataset, which classifies road surfaces as paved or unpaved across the United Arab Emirates. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It was last updated in March 2026.
Approximately 378,600 kilometers of roads in the Netherlands are mapped, with AI-derived estimates classifying 169,800 km as paved and 41,100 km as unpaved. This dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill information gaps. It was last updated in March 2026 and supports transportation and infrastructure analysis.
HeiGIT (Heidelberg Institute for Geoinformation Technology) provides a dataset detailing road surfaces in Comoros, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. It distinguishes paved and unpaved roads, covering approximately 0.0025 million kilometers of mapped roads, with AI-derived estimates for 11.6003% paved and 7.7376% unpaved. The dataset was last updated on March 2, 2026.
Approximately 125,600 km of roads are mapped in OpenStreetMap for Rwanda, with AI-derived estimates classifying 5.6% as paved and 28.1% 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.
Türkiye's road network, comprising approximately 1.22 million kilometers mapped in OpenStreetMap, is classified into paved and unpaved surfaces using a hybrid deep learning model. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), augments OSM data with predictions from Mapillary imagery and urban layers. It was last updated in March 2026.
A dataset from 2026 provides detailed road surface information for Albania, distinguishing paved and unpaved roads. It is derived from OpenStreetMap data and augmented with AI predictions from Mapillary imagery, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT). The dataset covers approximately 60,500 km of roads, with AI estimates classifying 18.9% as paved and 29.5% as unpaved.
Approximately 112,200 km of roads are mapped for Uruguay, with AI-derived estimates classifying 20,800 km as paved and 31,700 km as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It is intended to support transportation planning, infrastructure analysis, and climate emissions assessment.
Road surface data for Belgium, distinguishing paved and unpaved roads, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset covers approximately 0.2481 million kilometers of roads, with AI-derived estimates classifying 38.3796% as paved and 13.2798% 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 classifications for Romania, derived from OpenStreetMap data and augmented with deep learning predictions from Mapillary imagery. It covers approximately 0.4242 million kilometers of roads, with AI estimates classifying 26.2097% as paved and 24.1636% as unpaved. The dataset was last updated on March 2, 2026.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for the Marshall Islands. It combines OpenStreetMap (OSM) data with deep learning predictions from Mapillary imagery to distinguish paved and unpaved roads. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
0.01 million km of roads in Cabo Verde are mapped, with AI-derived estimates classifying 0.0014 million km as paved and 0.0014 million km as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers. It aims to address a gap where 0.0072 million km of road surface information is missing from OSM.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Mauritius. It distinguishes paved and unpaved roads, covering approximately 13.7 thousand kilometers of OpenStreetMap (OSM) data, with 29.2% paved and 4.5% unpaved. The data is augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.
HeiGIT's dataset provides AI-derived road surface classifications for Armenia, distinguishing paved and unpaved roads. It covers approximately 74,900 km of roads from OpenStreetMap, with 13.0% classified as paved and 11.6% as unpaved. The data was last updated on March 2, 2026.
Approximately 0.1136 million kilometers of roads from OpenStreetMap in Georgia are classified as paved or unpaved using a hybrid deep learning approach. The dataset, created by HeiGIT and last updated in March 2026, provides AI-derived estimates that 12.9% of roads are paved and 19.8% are unpaved, while 67.3% of surface information is missing from OSM. It is augmented with predictions from Mapillary imagery and urban classification layers.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for Pitcairn. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and PlanetScope satellite data to label roads as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and GIS applications.
HeiGIT's dataset provides detailed road surface information for Estonia, distinguishing paved from unpaved roads. It contains approximately 94,300 km of roads mapped in OpenStreetMap, with AI-derived estimates classifying 27.95% as paved and 15.43% as unpaved. The data was last updated on March 2, 2026, and combines OSM attributes with deep learning predictions from Mapillary imagery.
Canada's road network, covering approximately 2.11 million kilometers, is classified by surface type using a hybrid deep learning approach. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), distinguishes between paved and unpaved roads and was last updated in March 2026. It integrates OpenStreetMap data with AI predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.
Road surface data for Angola, distinguishing paved and unpaved roads, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset covers approximately 0.284 million kilometers of roads, with AI-derived estimates classifying 8.46% as paved and 38.90% as unpaved. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.