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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,039 datasets
Infracciones viales Palmira records traffic infractions imputed in the municipality of Palmira. The dataset includes categories, types, and codes of violations for the years 2022 and 2024. It is hosted on the Colombian open data portal www.datos.gov.co and was last updated on May 18, 2026.
Beginning in 2000, the Port Authority of New York and New Jersey annually reports motor vehicle crashes within its jurisdiction. The dataset includes yearly crash counts, categorized by facility and fatality status. It is published by data.ny.gov and was last updated in April 2026.
Reports on the status of national roads from the National Police Directorate of Traffic and Transportation. The dataset includes columns for the reason and type of road disruption, jurisdiction, traffic backup length, alternate routes, weather conditions, and municipality. It is hosted on the Colombian open data portal www.datos.gov.co and was last updated on 2026-05-18.
A geospatial dataset representing the administrative arterial road network of the City of Montreal, as defined by regulation 02-003. The dataset is provided by the Government and Municipalities of Québec and was last updated on 2026-04-22. It distinguishes roads under city council responsibility from those managed by borough councils.
Transport for NSW provides WCAG 2.0 compliant wayfinding maps for the Sydney and Parramatta light rail networks. The dataset covers stops on the L1 Dulwich Hill, L2 Randwick, L3 Kingsford, and L4 Westmead & Carlingford passenger lines. The data was last updated on 2026-04-27.
HeiGIT produced this dataset mapping 61,900 km of Nigerian arterial roads using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides surface type, width, and passability metrics for motorways, trunk, primary, and secondary road segments, filling significant gaps in existing OpenStreetMap data.
This dataset maps approximately 13,400 km of arterial roads in Denmark with AI-derived surface, width, and passability attributes. Produced by HeiGIT, it utilizes PlanetScope satellite imagery from 2020 and 2024 to augment OpenStreetMap data with high-resolution physical characteristics. The analysis focuses specifically on motorway, trunk, primary, and secondary road classifications.
HeiGIT produced this dataset of AI-derived surface, width, and passability metrics for 355,900 km of arterial roads in Brazil using 2020 and 2024 PlanetScope satellite imagery. It covers motorways, trunks, primary, and secondary roads, providing surface predictions for segments where OpenStreetMap tags are missing.
This dataset maps approximately 6,700 km of arterial roads in Jordan, providing AI-derived surface types and width classifications generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap attributes with deep-learning predictions to assess road passability for humanitarian logistics across motorway, trunk, primary, and secondary road classes.
HeiGIT produced this dataset mapping 21,200 km of arterial roads in Croatia 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) for segments classified as motorway, trunk, primary, and secondary in OpenStreetMap.
Mapping approximately 27,400 km of arterial roads in Venezuela, this dataset provides AI-derived surface types, widths, and passability scores from 2020 and 2024 PlanetScope imagery. Produced by HeiGIT, it integrates OpenStreetMap attributes with deep-learning predictions to assess national connectivity and humanitarian logistics.
Qatar arterial road network data covering 4,700 km was developed by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It provides AI-generated surface classifications, width estimates, and passability indices for segments classified as motorway, trunk, primary, or secondary in OpenStreetMap.
Mapping 33,800 km of arterial roads in Iraq, this dataset provides AI-derived surface and passability metrics generated by HeiGIT from 2020 and 2024 PlanetScope imagery. It combines OpenStreetMap (OSM) attributes with deep-learning predictions to fill data gaps in road surface and width classifications.
HeiGIT produced this dataset covering approximately 20,600 km of arterial roads in Latvia using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived classifications for road surface, width, and passability for segments classified in OpenStreetMap as motorway, trunk, primary, and secondary roads.
This dataset maps 20,300 km of arterial roads in Belgium, providing AI-derived surface types, widths, and passability scores produced by HeiGIT. It utilizes PlanetScope satellite imagery from 2020 and 2024 to classify motorways, trunks, and primary/secondary roads.
HeiGIT produced this dataset covering 0.2837 million km of arterial roads in the Russian Federation 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 in OpenStreetMap as motorway, trunk, primary, or secondary.
Romania road network data covering ~50,000 km of arterial roads, produced by HeiGIT using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides high-resolution predictions for road surface, width, and logistical passability to fill gaps in OpenStreetMap (OSM) data.
Supplying AI-derived road surface, width, and passability metrics for approximately 14,000 km of arterial roads in Sri Lanka, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It covers motorway, trunk, primary, and secondary road classes, filling gaps for the 39.8% of roads lacking surface tags in OpenStreetMap.
HeiGIT produced this dataset covering 45,600 km of arterial roads in Sweden using 2020 and 2024 PlanetScope satellite imagery. It combines OpenStreetMap geometry with AI-derived attributes for road surface, width, and logistical passability to fill data gaps in national infrastructure maps.
7,100 km of arterial road segments in Moldova with AI-derived surface and width attributes produced by HeiGIT using 2020 and 2024 PlanetScope imagery. The data covers motorways, trunks, primary, and secondary roads, filling gaps for the 58% of segments lacking surface tags in OpenStreetMap. The analysis provides a multi-temporal view of infrastructure changes and logistical accessibility.