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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,151 datasets
HeiGIT generated this dataset of approximately 900 km of arterial roads in Guadeloupe using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It supplements OpenStreetMap (OSM) data with deep-learning predictions for road surface, width, and a specialized Humanitarian Passability Index to fill gaps in existing mapping.
AI-derived road surface, width, and passability metrics for arterial roads in Svalbard and Jan Mayen Islands produced by HeiGIT using 2020 and 2024 PlanetScope imagery. The data covers segments classified in OpenStreetMap as motorway, trunk, primary, and secondary routes.
HeiGIT generated this dataset of AI-derived road surface, width, and passability metrics covering 16,400 km of arterial roads in Mozambique using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap (OSM) data by predicting surface types for the 18% of segments lacking tags, achieving 89.2% accuracy in surface classification.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 8,700 km of arterial roads in Botswana using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap data by providing surface predictions for segments previously lacking surface tags, covering motorways, trunks, primary, and secondary roads.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 1,900 km of arterial roads in Eswatini using 2020 and 2024 PlanetScope imagery. It integrates OpenStreetMap (OSM) segments with deep-learning predictions to fill gaps in existing infrastructure data. The analysis focuses on motorway, trunk, primary, and secondary road classes.
AI-derived surface, width, and passability metrics for 4,900 km of arterial roads in Kuwait, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It covers motorways, trunks, primary, and secondary roads, integrating OpenStreetMap attributes with deep-learning predictions to fill data gaps. The analysis includes a specific Humanitarian Passability Index (HPI) to assess logistical accessibility.
4,400 km of arterial roads in Armenia, providing AI-derived surface and width data from 2020 and 2024 PlanetScope satellite imagery. Produced by HeiGIT, it focuses on motorway, trunk, primary, and secondary road classes to assess national connectivity. The data fills significant gaps where OpenStreetMap (OSM) lacks surface tags, achieving 89.2% prediction accuracy.
HeiGIT generated this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Mayotte using 2020 and 2024 PlanetScope imagery. It covers approximately 200 km of infrastructure, integrating OpenStreetMap attributes with deep-learning predictions to fill data gaps in the region.
Delivering AI-derived road surface, width, and passability metrics for 13,700 km of arterial roads in Mali, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap attributes with deep-learning predictions to fill data gaps in the national transportation backbone.
AI-derived road surface, width, and passability metrics cover approximately 5,100 km of arterial roads in Nicaragua, produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. The data maps motorway, trunk, primary, and secondary road classes to fill gaps where OpenStreetMap (OSM) surface tags are missing.
HeiGIT produced this geospatial dataset covering approximately 1,000 km of arterial roads in Antarctica using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived classifications for road surface, width, and a Humanitarian Passability Index (HPI) for segments originally mapped in OpenStreetMap.
Supplying AI-derived road surface, width, and passability metrics for approximately 300 km of arterial roads in the United States Virgin Islands. Created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), the data utilizes PlanetScope satellite imagery from 2020 and 2024 to enhance OpenStreetMap (OSM) records.
Delivering AI-derived road surface, width, and passability metrics for approximately 300 kilometers of arterial roads in Comoros, developed by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It utilizes PlanetScope satellite imagery from 2020 and 2024 to fill gaps in OpenStreetMap (OSM) data, where over 60% of surface tags were previously missing. The analysis focuses on motorway, trunk, primary, and secondary road classifications.
Approximately 100km of arterial road segments in Greenland with AI-derived surface and width attributes produced by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data supplements OpenStreetMap (OSM) records for motorway, trunk, primary, and secondary roads. It provides predictions for road surface, width classes, and a logistical passability index where ground-truth data is missing.
Benin arterial road network analysis covering approximately 6,000 km, produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived surface types, width classifications, and passability indices for motorways, trunks, and primary or secondary roads. The dataset specifically fills attribute gaps for the 10.7% of Benin's arterial roads that lack surface tags in OpenStreetMap.
Arterial road metrics for Dominica, including AI-derived surface and width data, were produced by HeiGIT using 2020 and 2024 PlanetScope imagery. The data covers approximately 300 km of road segments, providing 89.2% accuracy for surface type predictions to fill gaps in OpenStreetMap coverage.
HeiGIT produced this geospatial dataset covering approximately 3,700 km of arterial roads in El Salvador using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides deep-learning predictions for road surface, width, and a Humanitarian Passability Index (HPI) to supplement OpenStreetMap (OSM) data. The analysis focuses on motorway, trunk, primary, and secondary road classes which form the national transportation backbone.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for Tuvalu's arterial road network using PlanetScope satellite imagery from 2020 and 2024. It supplements OpenStreetMap (OSM) data with deep-learning predictions for motorway, trunk, primary, and secondary road segments to fill metadata gaps.
Offering AI-derived road surface, width, and passability metrics for 16,600 km of arterial roads in Sudan, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It covers motorway, trunk, primary, and secondary road classes, filling surface data gaps for approximately 4,500 km of the network where OpenStreetMap tags were missing.
Offering AI-derived road surface, width, and passability metrics for approximately 700 km of arterial roads in Martinique, produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It integrates OpenStreetMap road segments with deep-learning predictions to assess logistical accessibility and surface transitions over a four-year period.