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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,284 datasets
Delivering AI-derived road surface, width, and passability metrics for approximately 9,700 km of arterial roads in Burkina Faso, produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) data to classify motorways, trunks, and primary/secondary roads. The analysis fills data gaps for 1,200 km of roads that previously lacked surface tags in OSM.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Macao SAR using 2020 and 2024 PlanetScope satellite imagery. It covers approximately 200 km of road segments, augmenting OpenStreetMap data with deep-learning predictions that fill gaps in existing surface tags.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for arterial roads in American Samoa using PlanetScope satellite imagery from 2020 and 2024. It covers approximately 100 km of road segments classified in OpenStreetMap as motorway, trunk, primary, or secondary. The data provides a standardized assessment of logistical accessibility for the territory's primary transportation backbone.
HeiGIT produced this dataset covering approximately 1,100 km of arterial roads in Suriname using PlanetScope satellite imagery from 2020 and 2024. It combines OpenStreetMap data with AI-derived predictions for road surface, width, and a Humanitarian Passability Index to fill infrastructure data gaps.
HeiGIT generated this geospatial dataset covering 400 km of arterial roads in the Faroe Islands using PlanetScope satellite imagery from 2020 and 2024. It integrates OpenStreetMap attributes with AI-derived predictions for road surface, width, and a Humanitarian Passability Index (HPI).
Giving access to AI-derived road surface, width, and passability metrics for approximately 500 km of arterial roads in Malta, produced by HeiGIT. It utilizes PlanetScope satellite imagery from 2020 and 2024 to supplement OpenStreetMap data with deep-learning predictions for segments lacking ground-truth tags.
Approximately 8,000 km of Somalia's arterial road network are mapped with AI-derived surface, width, and passability attributes by HeiGIT. The data combines 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap classifications to assess infrastructure changes and logistical accessibility.
HeiGIT produced this dataset mapping 12,800 km of arterial roads in the Central African Republic using PlanetScope satellite imagery from 2020 and 2024. It integrates AI-derived surface predictions, width classifications, and a Humanitarian Passability Index (HPI) with existing OpenStreetMap attributes.
HeiGIT generated these AI-derived road surface, width, and passability metrics for San Marino's arterial road network using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) segments with deep-learning predictions to provide consistent coverage of motorway, trunk, primary, and secondary roads. The analysis fills gaps in OSM surface tags with a model accuracy of 89.2%.
Approximately 9,300 km of arterial roads in South Sudan are mapped with AI-derived surface, width, and passability metrics produced by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data achieves 89.2% accuracy in surface classification, filling gaps for 46.7% of segments lacking OpenStreetMap tags.
HeiGIT developed this dataset providing AI-derived road surface, width, and passability metrics for approximately 400 km of arterial roads in Barbados using 2020 and 2024 PlanetScope imagery. It integrates OpenStreetMap (OSM) segments with deep-learning predictions to fill gaps where surface tags are missing or inaccurate.
HeiGIT produced this geospatial dataset covering approximately 5,500 km of arterial roads in Burundi 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 fill significant gaps in OpenStreetMap (OSM) data. The analysis focuses specifically on motorway, trunk, primary, and secondary road classes.
HeiGIT produced this dataset of AI-derived surface, width, and passability metrics for 8,400 km of Cambodian arterial roads using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap data by providing surface predictions for the 65.8% of segments that previously lacked tags, covering motorway, trunk, primary, and secondary road classes.
Produced by HeiGIT, this dataset provides AI-derived road surface, width, and passability metrics for arterial roads in Bonaire, Sint Eustatius, and Saba using 2020 and 2024 PlanetScope imagery. It covers approximately 100 km of the road network, integrating OpenStreetMap attributes with deep-learning predictions that achieved 89.2% accuracy.
Approximately 200 km of arterial roads in the Cayman Islands are described through AI-derived surface, width, and passability metrics. Created by HeiGIT, the data utilizes PlanetScope satellite imagery from 2020 and 2024 to analyze motorway, trunk, primary, and secondary road classes. The dataset integrates deep-learning predictions with existing OpenStreetMap attributes to provide a more complete view of national connectivity.
AI-derived road surface, width, and passability metrics cover approximately 3,700 km of arterial roads in Sierra Leone. Produced by HeiGIT, the data integrates OpenStreetMap attributes with deep-learning predictions generated from 2020 and 2024 PlanetScope satellite imagery. It specifically targets the national road backbone, including motorway, trunk, primary, and secondary classifications.
HeiGIT produced this dataset covering approximately 5,400 km of arterial roads in Costa Rica using PlanetScope satellite imagery from 2020 and 2024. It features AI-derived attributes for road surface, width, and a Humanitarian Passability Index (HPI) to supplement OpenStreetMap data. The analysis focuses on motorway, trunk, primary, and secondary road classes.
HeiGIT produced this dataset covering approximately 2,700 km of arterial roads in Haiti using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived classifications for road surface, width, and a Humanitarian Passability Index (HPI) to supplement missing OpenStreetMap (OSM) attributes. The analysis focuses on motorway, trunk, primary, and secondary road classes.
Geospatial data delineating Transit-Oriented Development (TOD) areas in the City of Laval, as illustrated in the revised urban development plan. The dataset is provided by the Government and Municipalities of Québec under a CC-BY-4.0 license and was last updated on 2026-04-22. Available file formats include SHP, GEOJSON, KML, and CSV.
Quarterly data from 2005 onward on the number of passengers using shared ride services at airports operated by the Port Authority of New York and New Jersey. The dataset is produced by the Port Authority from its Ground Transportation Information System (GTIS) and reflects passenger counts, which are registered separately from reservation numbers. It is hosted by data.ny.gov and was last updated in April 2026.