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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,731 datasets
OpenStreetMap road data for Myanmar, augmented with AI-derived surface classifications from Mapillary imagery. The dataset covers approximately 326,300 kilometers of roads, with AI predictions providing surface type for about 18% of the network. It was created by HeiGIT and last updated in March 2026.
HeiGIT's dataset provides detailed road surface information for Côte d'Ivoire, distinguishing between paved and unpaved roads. It covers approximately 0.1795 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 10.3493% as paved and 71.7843% as unpaved. The dataset was last updated on March 2, 2026.
Approximately 49,700 kilometers of roads are mapped in OpenStreetMap for Panama. 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.
HeiGIT's dataset provides AI-derived road surface classifications for Latvia, distinguishing paved and unpaved roads. It covers approximately 127,900 km of roads mapped in OpenStreetMap, with 19.5% paved and 32.5% unpaved according to model predictions. The data was last updated on March 2, 2026, and combines OSM attributes with deep learning predictions from Mapillary imagery.
Approximately 0.652 million kilometers of roads in Pakistan are mapped, with AI-derived estimates classifying 11.6% as paved and 9.2% as unpaved. This dataset from HeiGIT combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers to fill information gaps. It was last updated in March 2026 and is available in GeoJSON and GeoPackage formats.
Approximately 0.2839 million kilometers of roads in Uzbekistan are mapped, with AI-derived estimates classifying 7.3023% as paved and 5.936% as unpaved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OpenStreetMap data with deep learning predictions from Mapillary imagery and was last updated in March 2026. It aims to address a significant gap, as 86.7617% of road surface information is missing in the original OSM data.
HeiGIT's dataset provides detailed road surface classifications for Antigua and Barbuda, distinguishing paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and PlanetScope satellite data. Approximately 0.0023 million kilometers of roads are mapped, with AI-derived estimates for paved and unpaved lengths provided.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Jordan. It covers approximately 81,200 km of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
Road surface data for Venezuela (Bolivarian Republic of) distinguishes paved and unpaved roads. The dataset contains approximately 0.2997 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 15.0369% as paved and 22.9079% as unpaved. It was created by HeiGIT and last updated in March 2026.
Road surface data for San Marino derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary imagery. The dataset, created by HeiGIT, includes approximately 0.7 km of mapped roads, with AI-derived estimates classifying 42.9% as paved and 4.6% as unpaved. It was last updated on March 2, 2026.
Approximately 2.7 km of roads are mapped in OpenStreetMap for the Isle of Man, with AI-derived estimates classifying 36.1% as paved and 4.9% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It provides attributes for surface type, road characteristics, and urban/rural classification.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Afghanistan. It covers approximately 260,200 kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning model. The data is augmented with urban classification layers and street-level imagery predictions.
Approximately 1.2 km of roads are mapped in OpenStreetMap for Grenada, with AI-derived estimates classifying 22.438% as paved and 8.0695% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OSM data with deep learning predictions from Mapillary imagery and urban layers. It provides attributes for surface type, road characteristics, and urban classification.
Detailed road surface data for Poland, distinguishing 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, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).
94,200 kilometers of Honduran roads are mapped in OpenStreetMap, with AI-derived surface classifications for 44.8% of them. The Heidelberg Institute for Geoinformation Technology (HeiGIT) created this dataset by augmenting OSM data with deep learning predictions from Mapillary imagery and urban classification layers. It was last updated in March 2026.
0.0001 million kilometers of roads are mapped for the Holy See, with AI-derived estimates classifying 35.7427% as paved and 8.463% 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 55.7943% gap in OSM surface information.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for the Federated States of Micronesia. It distinguishes paved and unpaved roads, covering approximately 0.8 km of mapped roads, with 0.2 km classified as paved and 0.2 km as unpaved. The data originates from OpenStreetMap and is augmented with deep learning predictions from Mapillary imagery.
A geospatial dataset detailing road surfaces in Belarus, distinguishing paved from unpaved roads. It covers approximately 0.3754 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 22.13% as paved and 29.19% as unpaved. The dataset was created by HeiGIT using a hybrid deep learning approach and was last updated in March 2026.
HeiGIT's Namibia: Road Surface Data provides AI-derived classifications for approximately 201,600 kilometers of roads from OpenStreetMap. The dataset, last updated in March 2026, distinguishes paved from unpaved roads using a hybrid deep learning model applied to Mapillary imagery. It is augmented with urban classification layers from GHSU and AFRICAPOLIS.
A geospatial dataset detailing road surfaces across South Africa, derived from OpenStreetMap and augmented with AI predictions from Mapillary imagery. It covers approximately 1.05 million kilometers of roads, classifying them as paved or unpaved. The dataset was created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026.