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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,758 datasets
HeiGIT's dataset provides detailed road surface information for Cameroon, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. It covers approximately 226,600 km of roads, with AI estimates classifying 7.03% as paved and 48.685% as unpaved. The dataset was last updated on March 2, 2026.
0.5282 million kilometers of roads in Morocco are mapped, with AI-derived surface classifications for paved and unpaved segments. The dataset is created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) using a hybrid deep learning approach on Mapillary imagery and OSM data, last updated in March 2026. It aims to address a 67.479% gap in surface information within OpenStreetMap.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides detailed road surface information for Saint Lucia. It classifies approximately 2 km of roads from OpenStreetMap as paved or unpaved using a hybrid deep learning model, and includes AI-derived estimates for paved (0.7 km) and unpaved (0.4 km) lengths. The data is augmented with urban classification layers and supports transportation and GIS applications.
A geospatial dataset providing detailed information on road surfaces in Benin, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset includes approximately 0.09 million kilometers of mapped roads, with AI-derived estimates classifying 0.0083 million km as paved and 0.0324 million km as unpaved. It was created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Approximately 16,500 kilometers of roads are mapped in OpenStreetMap for Trinidad and Tobago. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach, augmented with urban classification layers. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Approximately 68,600 km of roads from OpenStreetMap are classified by surface type using a hybrid deep learning model. The dataset, created by HeiGIT and last updated in March 2026, provides AI-derived estimates that 5.4% of mapped roads are paved and 16.0% are unpaved, while 78.6% of surface information is missing from OSM.
Road surface data for Gambia derived from OpenStreetMap (OSM) and augmented with deep learning predictions from Mapillary imagery. The dataset includes approximately 0.0282 million kilometers of mapped roads, with AI-derived estimates classifying 6.6068% as paved and 21.4047% as unpaved. It was created by HeiGIT and last updated in March 2026.
Approximately 1.7949 million kilometers of roads in Spain are mapped, with AI-derived estimates classifying 0.3143 million km as paved and 0.1956 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 to fill gaps in surface information.
Approximately 511,500 kilometers of roads are mapped in OpenStreetMap for Colombia, with AI-derived estimates classifying 14.9% as paved and 27.0% 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 in March 2026.
HeiGIT's dataset provides detailed road surface information for Palau, 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 0.0005 million kilometers of roads are mapped, with AI-derived estimates classifying 9.2565% as paved and 22.6005% as unpaved.
Approximately 0.2478 million kilometers of roads are mapped for Libya, with AI-derived estimates classifying 23.1418% as paved and 22.9852% 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 and urban classification layers.
HeiGIT's dataset provides detailed road surface information for Thailand, classifying roads as paved or unpaved. It covers approximately 1.086 million kilometers of roads from OpenStreetMap, with AI-derived estimates filling gaps in the original data. The dataset was last updated on March 2, 2026.
HeiGIT's dataset provides detailed road surface information for Vanuatu, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, covering approximately 0.0049 million km of roads. It was last updated on March 2, 2026.
A geospatial dataset detailing road surfaces in the Turks and Caicos Islands, distinguishing paved and unpaved roads. The data originates from OpenStreetMap and is augmented with surface classifications derived from a hybrid deep learning model using Mapillary imagery. It was published by the Heidelberg Institute for Geoinformation Technology (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 Guernsey. It distinguishes paved and unpaved roads, covering approximately 1 km of roads mapped in OpenStreetMap, with 22.4% paved and 8.6% unpaved. The data is augmented with deep learning predictions from Mapillary imagery and urban classification layers.
Portugal's road network is detailed in this dataset, which classifies approximately 0.4063 million kilometers of roads as paved or unpaved. The Heidelberg Institute for Geoinformation Technology (HeiGIT) created it by augmenting OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.
Brunei Darussalam's road network is classified by surface type using a hybrid deep learning approach on OpenStreetMap and Mapillary data. The dataset covers approximately 0.006 million kilometers of roads, with AI-derived estimates for paved (0.0029 million km) and unpaved (0.0005 million km) segments. It was produced by HeiGIT and last updated in March 2026.
HeiGIT's dataset provides AI-derived road surface classifications for Oman, distinguishing paved from unpaved roads. It contains approximately 0.1164 million kilometers of roads from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery. The dataset was last updated on March 2, 2026.
Approximately 579,100 km of roads are mapped in OpenStreetMap for Kenya, with AI-derived estimates classifying 23,000 km as paved and 166,900 km as unpaved. The dataset, created by HeiGIT and last updated in March 2026, augments 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.
Approximately 16.9 thousand kilometers of roads are mapped for Belize, with AI-derived estimates classifying 11.5681% as paved and 88.4271% as unpaved. The dataset is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated in March 2026. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers.