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Traffic data, public transit, aviation, shipping, ride-hailing, accident records
8,284 datasets
Arterial road surface, width, and passability metrics for the Turks and Caicos Islands are derived from 2020 and 2024 PlanetScope satellite imagery by HeiGIT. The data covers approximately 100 km of road segments, integrating OpenStreetMap (OSM) attributes with deep-learning predictions to resolve missing surface tags.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 300 kilometers of arterial roads in Guam using 2020 and 2024 PlanetScope imagery. It supplements OpenStreetMap data, where over 56% of segments lack surface tags, by providing predictions with 89.2% accuracy.
AI-derived road surface, width, and passability metrics for arterial roads in the Solomon Islands, generated by HeiGIT using PlanetScope satellite imagery from 2020 and 2024. It covers motorway, trunk, primary, and secondary road classes, integrating OpenStreetMap attributes with deep-learning predictions that achieved 89.2% accuracy in surface classification.
Mapping approximately 5,000 km of arterial roads in Togo, this dataset provides AI-derived surface and width data from PlanetScope satellite imagery (2020 and 2024). Produced by HeiGIT, the records cover motorway, trunk, primary, and secondary road classes with associated passability scores. The analysis fills surface data gaps for approximately 900 km of roads that lack tags in OpenStreetMap.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for arterial roads in Montserrat using PlanetScope satellite imagery from 2020 and 2024. It classifies motorway, trunk, primary, and secondary road segments to provide consistent coverage where OpenStreetMap data is missing or incomplete.
AI-derived road surface, width, and passability metrics for approximately 5,700 km of arterial roads in Congo, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap (OSM) attributes with deep-learning predictions to fill gaps where surface tags are missing, covering motorway, trunk, primary, and secondary road classes.
9,200 km of arterial roads in Turkmenistan with AI-derived surface, width, and passability metrics produced by HeiGIT. The data utilizes PlanetScope satellite imagery from 2020 and 2024 to supplement OpenStreetMap (OSM) attributes for motorway, trunk, primary, and secondary road classes.
This dataset maps approximately 600 km of arterial roads in French Polynesia, providing AI-derived surface types and width classes generated by HeiGIT. It utilizes PlanetScope satellite imagery from 2020 and 2024 to classify motorway, trunk, primary, and secondary road segments. The data includes a Humanitarian Passability Index (HPI) to assess logistical accessibility across the island network.
Seychelles arterial road network data featuring AI-predicted surface types and passability metrics derived from 2020 and 2024 PlanetScope imagery. Created by HeiGIT, the dataset covers approximately 100 km of motorway, trunk, primary, and secondary road segments to fill gaps in OpenStreetMap (OSM) attributes.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 600 km of arterial roads in French Guiana using 2020 and 2024 PlanetScope satellite imagery. It supplements OpenStreetMap (OSM) data by providing surface predictions for segments where tags were previously missing.
HeiGIT generated this geospatial dataset of road surface, width, and passability metrics for approximately 100 km of arterial roads in Tonga. Using PlanetScope satellite imagery from 2020 and 2024, the analysis provides deep-learning predictions for segments where OpenStreetMap data is incomplete.
Supplying AI-derived road attributes for approximately 1,100 km of arterial roads in Brunei Darussalam, produced by HeiGIT using 2020 and 2024 PlanetScope satellite imagery. It integrates OpenStreetMap geometry with deep-learning predictions for surface type, width, and logistical passability across motorway, trunk, primary, and secondary road classes.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for Palau's arterial road network using PlanetScope satellite imagery from 2020 and 2024. It maps approximately 100 km of roads, supplementing OpenStreetMap data with deep-learning predictions that achieve 89.2% accuracy.
HeiGIT produced this dataset covering approximately 1,300 km of arterial roads in Réunion using 2020 and 2024 PlanetScope satellite imagery. It combines OpenStreetMap attributes with AI-derived predictions for road surface, width, and humanitarian accessibility to fill gaps in existing map data.
HeiGIT produced this dataset of AI-derived road surface, width, and passability metrics for approximately 200 km of arterial roads in Jersey using PlanetScope satellite imagery from 2020 and 2024. It integrates OpenStreetMap attributes with deep-learning predictions to fill data gaps, specifically targeting motorway, trunk, primary, and secondary road classes.
Monaco arterial road network data featuring AI-derived surface types, widths, and passability scores generated by HeiGIT. The dataset utilizes PlanetScope satellite imagery from 2020 and 2024 to augment OpenStreetMap road segments with deep-learning predictions. It specifically covers motorway, trunk, primary, and secondary road classes.
HeiGIT generated this dataset of road surface, width, and passability metrics for 1,400 km of arterial roads in Mauritius using 2020 and 2024 PlanetScope satellite imagery. It combines deep-learning predictions with OpenStreetMap attributes to provide a 89.2% accurate assessment of road conditions for motorway, trunk, primary, and secondary classifications.
HeiGIT generated this dataset of AI-derived road surface, width, and passability metrics for arterial roads in the Cook Islands using PlanetScope satellite imagery from 2020 and 2024. It covers approximately 100 km of road segments classified as motorway, trunk, primary, or secondary in OpenStreetMap.
Giving access to AI-derived road surface, width, and passability metrics for approximately 6,800 km of arterial roads in the Dominican Republic. Produced by HeiGIT using PlanetScope satellite imagery from 2020 and 2024, it supplements OpenStreetMap (OSM) data with deep-learning predictions. The analysis focuses on motorway, trunk, primary, and secondary road classifications.
Delivering AI-derived road surface, width, and passability metrics for approximately 9,700 km of arterial roads in Burkina Faso, produced by HeiGIT. It integrates 2020 and 2024 PlanetScope satellite imagery with OpenStreetMap (OSM) data to classify motorways, trunks, and primary/secondary roads. The analysis fills data gaps for 1,200 km of roads that previously lacked surface tags in OSM.