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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,040 datasets
61,000 km of arterial roads in Ukraine with AI-derived surface and width attributes produced by HeiGIT using PlanetScope imagery from 2020 and 2024. The dataset covers motorways, trunks, primary, and secondary roads, filling gaps where OpenStreetMap (OSM) surface tags are missing for approximately 16,900 km of the network. It provides a Humanitarian Passability Index (HPI) to assess logistical accessibility for heavy transport.
HeiGIT generated this geospatial dataset covering approximately 9,000 km of arterial roads in Iceland using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides deep-learning predictions for road surface, width, and humanitarian passability for motorway, trunk, primary, and secondary road classes.
Covering 14,400 km of arterial roads in Serbia, this dataset provides AI-derived surface, width, and passability metrics generated by HeiGIT from 2020 and 2024 PlanetScope imagery. It integrates OpenStreetMap attributes with deep-learning predictions to fill data gaps for approximately 19% of the network lacking surface tags.
HeiGIT generated this dataset covering 46,600 km of arterial roads in the Philippines using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived classifications for road surface, width, and passability to supplement OpenStreetMap data.
This dataset maps approximately 19,800 km of arterial roads in Bangladesh, providing AI-derived surface types, widths, and passability scores produced by HeiGIT. Using PlanetScope satellite imagery from 2020 and 2024, the data supplements OpenStreetMap (OSM) attributes with deep-learning predictions that achieve 89.2% accuracy.
HeiGIT generated this geospatial dataset covering 1.76 million km of arterial roads in China using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived attributes for road surface, width, and passability for segments classified as motorway, trunk, primary, and secondary in OpenStreetMap.
Mapping 21,100 km of arterial roads in Kenya, this dataset provides AI-derived surface types, widths, and passability scores generated by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap attributes to classify motorway, trunk, primary, and secondary road segments.
Offering AI-derived attributes for 104,100 km of arterial roads in the United Kingdom, developed by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It combines OpenStreetMap data with deep-learning analysis of PlanetScope satellite imagery from 2020 and 2024. The records detail road surface types, width classes, and logistical passability scores for motorways, trunks, and primary/secondary roads.
This dataset maps approximately 26,200 km of arterial roads in Belarus, providing AI-derived surface types, widths, and passability scores generated by HeiGIT. It utilizes PlanetScope satellite imagery from 2020 and 2024 to identify road surface changes and logistical accessibility for humanitarian planning.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for 109,700 km of arterial roads in Argentina using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap data by providing surface predictions for segments lacking tags, achieving a validated accuracy of 89.2%.
A source of AI-derived road surface, width, and passability metrics for approximately 9,100 km of arterial roads in Bosnia and Herzegovina. Created by HeiGIT, it integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap attributes to fill infrastructure data gaps.
HeiGIT produced this geospatial dataset covering 68,100 km of arterial roads in Vietnam 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 attributes.
HeiGIT produced this dataset mapping 1.389 million km of arterial roads in the United States using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived attributes for road surface, width, and a Humanitarian Passability Index (HPI) for segments classified as motorway, trunk, primary, or secondary in OpenStreetMap.
Produced by HeiGIT, this dataset contains AI-derived surface, width, and passability metrics for 5,500 km of Slovenian arterial roads based on 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap data by providing surface predictions for segments lacking tags, achieving a validated accuracy of 89.2%.
Spanning 131,300 km of arterial roads in Spain, providing AI-derived surface types and width classifications generated by HeiGIT from 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) geometry with deep-learning predictions to fill gaps where OSM surface tags are missing for nearly 50% of the network.
Approximately 79,300 km of Thailand's arterial road network are mapped with AI-derived surface and width attributes produced by HeiGIT from 2020 and 2024 PlanetScope imagery. This resource provides surface predictions for 41,200 km of roads that currently lack surface tags in OpenStreetMap.
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 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.
HeiGIT produced this geospatial dataset covering approximately 69,700 km of arterial roads in Pakistan using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides surface type, width, and passability metrics for motorway, trunk, primary, and secondary road classes, filling gaps where OpenStreetMap data is missing.
HeiGIT produced this dataset covering approximately 3,800 km of arterial roads in Cyprus using PlanetScope satellite imagery from 2020 and 2024. It provides AI-derived attributes for road surface, width, and passability across motorway, trunk, primary, and secondary classifications.