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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,040 datasets
Mapping 20,500 km of arterial roads in the Syrian Arab Republic, this dataset provides AI-derived surface types, widths, and passability scores for 2020 and 2024. Developed by HeiGIT using PlanetScope satellite imagery, it fills gaps for the 87.6% of local OpenStreetMap segments that lack surface tags. The data achieves 89.2% accuracy in surface classification, significantly exceeding the 64.7% accuracy of existing crowdsourced tags.
This dataset maps approximately 9,500 km of arterial roads in Guatemala using AI-derived predictions from PlanetScope satellite imagery and OpenStreetMap data. Produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT), it provides road surface, width, and passability metrics for the years 2020 and 2024. The analysis focuses on high-capacity transportation segments including motorways, trunk, primary, and secondary roads.
HeiGIT generated this geospatial dataset covering 38,000 km of arterial roads in Portugal using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides high-resolution predictions for road surface, width, and passability for segments classified as motorway, trunk, primary, and secondary in OpenStreetMap.
Stretching across approximately 15,200 km of arterial roads in Tunisia, providing AI-derived surface types, widths, and passability scores generated by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) data to identify infrastructure changes and fill attribute gaps.
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.
HeiGIT produced this dataset mapping 17,700 km of arterial roads in Taiwan using AI analysis of PlanetScope satellite imagery from 2020 and 2024. It provides surface type, width classifications, and passability indices for motorway, trunk, primary, and secondary road segments.
This dataset maps 229,100 km of arterial roads in France, 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. The data fills critical gaps where OpenStreetMap (OSM) surface tags are missing, achieving 89.2% classification accuracy.
Offering AI-derived road attributes for 28,400 km of arterial roads in Austria, generated by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It includes surface type, width classifications, and a Humanitarian Passability Index (HPI) for segments classified as motorway, trunk, primary, and secondary in OpenStreetMap.
A source of AI-derived road surface, width, and passability metrics for 176,600 km of arterial roads in Germany, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It covers motorways, trunks, primary, and secondary roads, filling gaps where OpenStreetMap (OSM) surface tags are missing. The data includes deep-learning predictions for surface types and a logistical accessibility index.
This dataset maps approximately 35,200 km of arterial roads in Colombia, providing AI-derived surface types, widths, and passability scores produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap data to identify road surface changes and logistical accessibility. The analysis focuses on motorway, trunk, primary, and secondary road classifications.
HeiGIT produced this dataset containing AI-derived road surface, width, and passability metrics for approximately 5,000 km of arterial roads in Estonia using PlanetScope satellite imagery from 2020 and 2024. It combines OpenStreetMap (OSM) segment data with deep-learning predictions to provide consistent coverage across motorway, trunk, primary, and secondary road classes.
HeiGIT produced this dataset covering 29,300 km of arterial roads in Myanmar using 2020 and 2024 PlanetScope satellite imagery. It provides AI-derived classifications for road surface, width, and humanitarian passability to supplement missing OpenStreetMap (OSM) attributes. The analysis focuses on motorway, trunk, primary, and secondary road classes.
HeiGIT mapped 14,600 km of arterial roads in Switzerland using PlanetScope satellite imagery from 2020 and 2024. The records include AI-derived classifications for road surface, width, and a Humanitarian Passability Index (HPI) to assess logistical accessibility.
This dataset maps approximately 14,600 km of arterial roads in Ghana, providing AI-derived surface types, widths, and passability scores from PlanetScope satellite imagery. Produced by HeiGIT, it covers arterial road classes including motorway, trunk, primary, and secondary segments with temporal comparisons between 2020 and 2024.
Slovakia arterial road network analysis covering 9,400 km, featuring AI-derived surface and width attributes generated by HeiGIT from 2020 and 2024 PlanetScope imagery. The data combines OpenStreetMap (OSM) geometry with deep-learning predictions to assess logistical accessibility and infrastructure changes over a four-year period.
This dataset maps 1,900 km of arterial roads in Hong Kong SAR using AI-derived attributes from 2020 and 2024 PlanetScope satellite imagery. Produced by HeiGIT, it provides surface type, width, and passability metrics to supplement OpenStreetMap data where surface tags are missing for 28% of the network.
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 produced this dataset covering 68,800 km of arterial roads in the Republic of Korea using PlanetScope satellite imagery from 2020 and 2024. It features AI-derived attributes for road surface, width, and a Humanitarian Passability Index (HPI) to fill significant data gaps in OpenStreetMap.
AI-derived infrastructure metrics for approximately 32,700 km of arterial roads in Morocco using PlanetScope satellite imagery from 2020 and 2024. Developed by the Heidelberg Institute for Geoinformation Technology (HeiGIT), it integrates OpenStreetMap (OSM) segments with deep-learning predictions for surface type, width, and logistical accessibility. The analysis focuses specifically on motorway, trunk, primary, and secondary road classes.