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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,284 datasets
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 12,600 km of arterial roads in Côte d'Ivoire. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap data to classify motorways, trunks, and primary/secondary routes.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for Gibraltar's arterial roads using PlanetScope satellite imagery from 2020 and 2024. It classifies motorway, trunk, primary, and secondary road segments to provide a Humanitarian Passability Index (HPI) for logistical planning.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 900 km of arterial roads in Djibouti using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to provide consistent coverage across motorway, trunk, primary, and secondary road classes.
HeiGIT generated this geospatial dataset for Guernsey's arterial roads using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It maps approximately 100 km of road segments, providing surface types, width classes, and passability scores to supplement OpenStreetMap data. The analysis focuses on motorway, trunk, primary, and secondary road classifications.
HeiGIT generated this geospatial dataset covering approximately 200 km of arterial roads in Saint Lucia using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides road surface classifications, width estimates, and passability indices for segments classified as motorway, trunk, primary, or secondary in OpenStreetMap.
Covering approximately 2,800 km of arterial roads in Equatorial Guinea, providing AI-derived surface and width classifications produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) geometries to fill data gaps in the national transportation network.
Micronesia's 200 km arterial road network is mapped with AI-derived surface, width, and passability attributes generated by HeiGIT from 2020 and 2024 PlanetScope imagery. The data covers segments classified in OpenStreetMap as motorway, trunk, primary, and secondary roads, providing 100% coverage for previously missing surface tags.
AI-derived road surface, width, and passability metrics for approximately 4,100 km of arterial roads in Panama are provided by the Heidelberg Institute for Geoinformation Technology (HeiGIT). The data integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap attributes to classify motorways, trunk, primary, and secondary roads.
Delivering AI-derived road surface, width, and passability attributes for approximately 5,700 km of arterial roads in Mauritania. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), the data utilizes PlanetScope satellite imagery from 2020 and 2024 to enhance OpenStreetMap (OSM) records. It specifically targets motorway, trunk, primary, and secondary road classes which form the country's national connectivity backbone.
Bhutan's arterial road network covering approximately 2,300 km with AI-derived surface, width, and passability attributes. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), the data utilizes PlanetScope satellite imagery from 2020 and 2024 to fill gaps in OpenStreetMap. It focuses on high-capacity transportation routes including motorways, trunks, and primary and secondary roads.
HeiGIT produced this geospatial dataset providing AI-derived road surface, width, and passability metrics for arterial roads on Christmas Island using PlanetScope satellite imagery from 2020 and 2024. It supplements OpenStreetMap (OSM) data with deep-learning predictions to identify paved and unpaved segments across the island's primary transport network.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Saint Vincent and the Grenadines using 2020 and 2024 PlanetScope satellite imagery. It maps approximately 100 km of road segments, providing higher accuracy for surface types than standard OpenStreetMap tags. The analysis focuses on motorway, trunk, primary, and secondary road classifications.
HeiGIT generated this dataset mapping 10,700 km of arterial roads in Niger using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived surface types, width classes, and passability indices for motorway, trunk, primary, and secondary road segments.
AI-derived road attributes for 1,700 km of arterial roads in Timor-Leste, produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It maps surface types, width classes, and passability scores for segments classified in OpenStreetMap as motorway, trunk, primary, and secondary roads.
Covering approximately 900 km of arterial roads in Bahrain, featuring AI-derived surface types and width classifications. Developed by HeiGIT, it utilizes PlanetScope satellite imagery from 2020 and 2024 to augment OpenStreetMap data for motorway, trunk, primary, and secondary road classes.
Mapping approximately 5,000 km of arterial roads in Togo, this dataset provides AI-derived surface and width data from PlanetScope satellite imagery (2020 and 2024). Produced by HeiGIT, the records cover motorway, trunk, primary, and secondary road classes with associated passability scores. The analysis fills surface data gaps for approximately 900 km of roads that lack tags in OpenStreetMap.
AI-derived road surface, width, and passability metrics for arterial roads in the Solomon Islands, generated by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It covers motorway, trunk, primary, and secondary road classes, integrating OpenStreetMap attributes with deep-learning predictions that achieved 89.2% accuracy in surface classification.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 600 km of arterial roads in French Guiana using 2020 and 2024 PlanetScope satellite imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for segments where tags were previously missing.
HeiGIT produced this dataset covering approximately 7,500 km of arterial roads in Kyrgyzstan using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides high-resolution predictions for road surface, width, and humanitarian passability for segments classified as motorway, trunk, primary, and secondary in OpenStreetMap.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 300 kilometers of arterial roads in Guam using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap data, where over 56% of segments lack surface tags, by providing predictions with 89.2% accuracy.