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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,113 datasets
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 200 km of arterial roads in Aruba using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) segments with deep-learning predictions to fill gaps where surface tags are missing.
HeiGIT generated this geospatial dataset covering approximately 200 km of arterial roads in Sao Tome and Principe using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) data with AI-derived predictions for road surface type, width, and logistical passability. The analysis focuses on motorway, trunk, primary, and secondary road classifications.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for Saint Barthélemy's arterial road network using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap (OSM) data with deep-learning predictions for motorway, trunk, primary, and secondary road segments.
HeiGIT produced this geospatial dataset providing AI-derived road surface, width, and passability metrics for arterial roads in Saint Martin (French part) using 2020 and 2024 PlanetScope imagery. It classifies motorway, trunk, primary, and secondary road segments to support humanitarian logistics and infrastructure monitoring.
Approximately 14,700 km of arterial roads in Mongolia, providing AI-derived surface types and width classifications generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) road segments with deep-learning predictions for surface material, width-based capacity, and a Humanitarian Passability Index (HPI).
Anguilla arterial road network data featuring AI-derived surface types, widths, and passability scores for approximately 100 kilometers of infrastructure. Created by HeiGIT, the dataset utilizes PlanetScope satellite imagery from 2020 and 2024 to augment OpenStreetMap attributes with deep-learning predictions. It specifically targets motorway, trunk, primary, and secondary road classifications.
Giving access to AI-derived attributes for approximately 8,700 km of arterial roads in Uruguay, produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It maps road surface types, width classifications, and logistical accessibility for segments classified in OpenStreetMap as motorway, trunk, primary, and secondary roads.
This dataset maps 13,700 km of arterial roads in Zimbabwe, providing AI-derived surface types, widths, and passability scores generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It covers motorway, trunk, primary, and secondary road classes, filling gaps where OpenStreetMap (OSM) surface tags are missing for approximately 3,500 km of the network.
This geospatial dataset provides AI-derived road surface, width, and passability metrics for approximately 1,200 km of arterial roads in Gambia. Produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery, it supplements OpenStreetMap (OSM) data with deep-learning predictions for motorway, trunk, primary, and secondary road classes.
HeiGIT produced this dataset of AI-derived surface, width, and passability metrics for approximately 6,400 km of arterial roads in Honduras using 2020 and 2024 PlanetScope satellite imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for the 21% of segments lacking tags, focusing on motorway, trunk, primary, and secondary classifications.
A collection of AI-derived road surface, width, and passability metrics for approximately 800 km of arterial roads in the Bahamas, generated by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for segments lacking official tags, achieving a validated accuracy of 89.2%.
Covering approximately 100 kilometers of arterial roads in Liechtenstein, this dataset provides AI-derived surface and width metrics produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap attributes to assess infrastructure changes and humanitarian accessibility.
A collection of AI-derived road surface, width, and passability metrics for approximately 300 km of arterial roads in Andorra, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill gaps in existing road infrastructure data, achieving 89.2% accuracy for surface classification.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Saint Pierre and Miquelon using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for segments lacking tags, achieving 89.2% accuracy in surface classification.
HeiGIT produced this dataset mapping 5,300 km of arterial roads in Gabon using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides high-resolution predictions for road surface, width, and logistical passability for motorway, trunk, primary, and secondary road classes.
A source of AI-derived road surface, width, and passability metrics for arterial roads in the British Virgin Islands, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It covers approximately 100 km of road segments classified in OpenStreetMap as motorway, trunk, primary, or secondary.
Ranging over approximately 9,700 km of arterial roads in Senegal, providing AI-derived surface types and width classifications produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) data to assess road passability and infrastructure changes over a four-year period.
Delivering AI-derived road surface, width, and passability metrics for approximately 700 km of arterial roads in Cabo Verde, produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) data to classify motorways, trunks, and primary/secondary roads.
HeiGIT produced this dataset of AI-derived road attributes for approximately 1,000 km of arterial roads in Guyana using 2020 and 2024 PlanetScope satellite imagery. It maps surface types, width classifications, and passability scores for segments classified in OpenStreetMap as motorway, trunk, primary, or secondary. The data provides a multi-temporal view of infrastructure changes over a four-year period.
HeiGIT produced this dataset covering approximately 2,500 km of arterial roads in Jamaica using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides road surface types, width classifications, and a passability index to fill gaps in OpenStreetMap (OSM) data. The analysis focuses on motorway, trunk, primary, and secondary road classes.