Loading...
Loading...
Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,040 datasets
Approximately 11,100 km of Nepal's arterial roads are mapped with AI-derived surface and width attributes generated by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data provides surface predictions and a Humanitarian Passability Index for motorway, trunk, primary, and secondary road classes.
14,400 km of arterial road segments in Oman feature AI-derived surface and width attributes generated by HeiGIT from 2020 and 2024 PlanetScope imagery. The data supplements OpenStreetMap by providing surface predictions for the 48.2% of segments that lack existing tags. It focuses on motorways, trunks, primary, and secondary roads to assess national connectivity.
HeiGIT produced this mapping of approximately 39,600 km of arterial roads in Chile using 2020 and 2024 PlanetScope satellite imagery. It provides AI-derived classifications for road surface, width, and a Humanitarian Passability Index (HPI) for motorway, trunk, primary, and secondary road classes.
Approximately 21,900 km of arterial roads in Angola, providing AI-derived surface types, widths, and passability scores generated by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) attributes to track infrastructure changes and logistical accessibility.
HeiGIT produced this dataset covering 57,100 km of arterial roads in Norway using PlanetScope satellite imagery from 2020 and 2024. It features AI-derived attributes for road surface, width, and a Humanitarian Passability Index (HPI) for segments classified as motorway, trunk, primary, and secondary roads.
HeiGIT generated this dataset covering 6,800 km of arterial roads in Rwanda using PlanetScope satellite imagery from 2020 and 2024. It features AI-derived predictions for road surface, width, and a Humanitarian Passability Index (HPI) for segments classified as motorway, trunk, primary, and secondary.
HeiGIT produced this dataset covering approximately 7,300 km of arterial roads in Georgia using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived classifications for road surface, width, and passability to supplement OpenStreetMap data. The analysis focuses specifically on motorway, trunk, primary, and secondary road classes.
This dataset maps 0.55 million km of arterial roads in India, providing AI-derived surface types, widths, and passability scores produced by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data fills surface information gaps for approximately 68.9% of the national arterial network that lacks OpenStreetMap tags.
This dataset maps 21,100 km of arterial roads in Libya, providing AI-derived surface types, widths, and passability scores from 2020 and 2024 PlanetScope satellite imagery. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), it focuses on motorway, trunk, primary, and secondary road classes to assess national connectivity.
Covering approximately 30,500 km of arterial roads in Hungary, providing AI-derived surface types, widths, and passability scores generated by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) attributes to fill gaps where surface tags are missing. The data focuses on motorways, trunks, primary, and secondary roads that form the national transportation backbone.
HeiGIT produced this dataset covering 20,500 km of arterial roads in Uzbekistan 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.
15,100 km of arterial roads in Yemen are mapped with AI-derived surface and width attributes generated by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data provides a Humanitarian Passability Index (HPI) and identifies surface transitions from unpaved to paved. This analysis focuses on motorways, trunks, and primary/secondary roads that form the national transportation backbone.
HeiGIT produced this dataset mapping approximately 9,500 km of arterial roads in Israel using 2020 and 2024 PlanetScope satellite imagery. It features AI-derived classifications for road surface, width, and passability across motorway, trunk, primary, and secondary road types.
HeiGIT produced this dataset mapping approximately 4,200 km of arterial roads in Puerto Rico using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides road surface types, width classifications, and passability scores for motorway, trunk, primary, and secondary road segments.
HeiGIT generated this dataset of AI-derived road surface, width, and passability metrics for 49,500 km of arterial roads in Egypt. It utilizes PlanetScope satellite imagery from 2020 and 2024 to classify motorways, trunk, primary, and secondary road segments. The analysis provides surface information for 47,300 km of roads that previously lacked surface tags in OpenStreetMap.
This dataset maps 117,800 km of arterial roads in Saudi Arabia using AI-derived attributes from PlanetScope satellite imagery captured in 2020 and 2024. Produced by HeiGIT, it provides surface type, width classes, and passability scores for motorway, trunk, primary, and secondary road segments. The analysis fills critical data gaps where OpenStreetMap (OSM) tags are missing, achieving 89.2% accuracy in surface prediction.
Delivering AI-derived road surface, width, and passability metrics for approximately 121,400 km of arterial roads in Indonesia, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It covers motorway, trunk, primary, and secondary road classes, filling data gaps for the 66% of Indonesian OpenStreetMap segments that lack surface tags. The analysis utilizes a deep-learning model with 89.2% accuracy to classify infrastructure across the archipelago.
HeiGIT produced this dataset covering 20,900 km of arterial roads in Ireland using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides road surface predictions, width classifications, and a passability index to supplement existing OpenStreetMap data. The analysis focuses on motorway, trunk, primary, and secondary road classes.
An overview of tourist transport services in Quebec, including airports, stations, rental companies, and ferries. Data originates from the Quebec Tourism Information System (SIT Quebec). The dataset is not a complete inventory of all available services.
HeiGIT produced this dataset covering 27,800 km of arterial roads in Bolivia using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides high-resolution predictions for road surface, width, and a specialized Humanitarian Passability Index (HPI) to supplement OpenStreetMap data.