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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,045 datasets
Montenegro arterial road network data featuring AI-derived surface and width attributes generated by HeiGIT from 2020 and 2024 PlanetScope imagery. It covers approximately 2,200 km of roads classified as motorway, trunk, primary, and secondary in OpenStreetMap.
Maps 160,100 km of arterial roads in Mexico using AI-derived attributes from PlanetScope satellite imagery (2020 and 2024) and OpenStreetMap data. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), it provides surface type, width, and passability metrics for motorways, trunks, and primary/secondary roads. The dataset fills significant gaps where OpenStreetMap surface tags are missing.
HeiGIT generated this geospatial dataset covering approximately 25,500 km of arterial roads in Czechia using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived classifications for road surface, width, and logistical passability to supplement OpenStreetMap data where tags are missing.
HeiGIT produced this dataset covering 64,800 km of arterial roads in Peru using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides high-resolution predictions for road surface, width, and logistical passability to supplement existing OpenStreetMap data.
Poland arterial road network data covering 61,400 km with AI-derived surface and width attributes produced by HeiGIT. The dataset integrates PlanetScope satellite imagery from 2020 and 2024 to assess infrastructure changes and logistical accessibility.
29,100 km of arterial roads in Tanzania analyzed by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. The data provides AI-derived surface types, width classifications, and passability scores for motorway, trunk, primary, and secondary road segments.
HeiGIT produced this dataset covering 190,600 km of Australian arterial roads 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 gaps in OpenStreetMap data.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 4,600 km of arterial roads in Albania using PlanetScope satellite imagery from 2020 and 2024. It combines OpenStreetMap (OSM) segment IDs with deep-learning predictions to provide a more complete view of national transportation infrastructure than standard crowdsourced tags.
Offering AI-derived road surface, width, and passability metrics for 227,000 km of arterial roads in Japan, produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It utilizes PlanetScope satellite imagery from 2020 and 2024 to supplement OpenStreetMap data for motorway, trunk, primary, and secondary road classes. The analysis identifies surface types and transitions with 89.2% accuracy, significantly exceeding standard OSM tag reliability.
HeiGIT produced this dataset mapping 75,300 km of Algerian arterial roads using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It supplements OpenStreetMap data with predicted surface types, road widths, and a logistical passability index for motorways, trunks, and primary/secondary roads.
Delivering AI-derived surface types, widths, and passability scores for approximately 10,900 km of arterial roads in Bulgaria. Created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), it utilizes PlanetScope satellite imagery from 2020 and 2024 to augment OpenStreetMap (OSM) data. The analysis focuses on motorways, trunks, and primary/secondary roads that form the national transportation backbone.
Covering 119,200 km of arterial roads in Turkey, this dataset provides AI-derived surface and width predictions generated by HeiGIT. It utilizes PlanetScope satellite imagery from 2020 and 2024 to supplement OpenStreetMap attributes with logistical accessibility metrics.
Supplying AI-derived surface and width attributes for approximately 49,000 km of arterial roads in Kazakhstan, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It supplements OpenStreetMap (OSM) data by filling surface tag gaps for 24.6% of the network with predictions that achieve 89.2% accuracy. The data covers motorway, trunk, primary, and secondary road classes and their associated links.
Offering AI-derived road attributes for approximately 24,500 km of arterial roads in Cameroon, generated by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It includes surface type, road width, and a Humanitarian Passability Index (HPI) for segments classified in OpenStreetMap as motorway, trunk, primary, and secondary. The analysis fills critical data gaps, providing surface information for 489% more road length than is currently tagged in OSM.
Supplying AI-derived attributes for 231,400 km of arterial roads in Canada, produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It maps road surface types, width classes, and a Humanitarian Passability Index (HPI) for segments classified in OpenStreetMap as motorway, trunk, primary, and secondary roads.
HeiGIT produced this dataset covering 39,600 km of arterial roads in Greece using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived surface types, road widths, and passability scores for motorways, trunks, and primary/secondary roads to fill gaps in existing infrastructure maps.
HeiGIT produced this dataset covering 16,200 km of arterial roads in the United Arab Emirates using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived surface types, width classifications, and passability scores to supplement OpenStreetMap data.
Offering AI-derived road surface, width, and passability metrics for approximately 3,200 km of arterial roads in Luxembourg, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to assess logistical accessibility and infrastructure changes over a four-year period.
This dataset maps 33,200 km of arterial roads in Finland, providing AI-derived surface types and width classifications generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill surface tag gaps for approximately 10.8% of the national arterial network. The data focuses on motorway, trunk, primary, and secondary road classes and their links.
Approximately 39,100 km of arterial roads in Malaysia, providing AI-derived surface types and width classifications generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill data gaps for the 44.7% of arterial roads that lack surface tags in the original OSM records.