image: Image on the right: © sig_3co, Panoramax; Figure on the left illustrates the workflow and challenges of extracting road attributes from 3-4m PlanetScope imagery.
Credit: Image on the right: © sig_3co, Panoramax
While many global road maps exist, few include detailed surface information or keep pace with rapid infrastructure change. The new HeiGIT dataset closes this gap by combining 3–4 meter resolution PlanetScope imagery (2020–2024) with deep-learning models to analyze 9.2 million kilometers of major transport routes connecting cities and rural regions. The result is a high-accuracy global classification (89.2%), outperforming widely used open datasets by over 20 percentage points.
A central component of the dataset is the Humanitarian Passability Score — an index that combines surface type and width to estimate accessibility under varying conditions. This metric supports better routing for logistics, infrastructure management, and emergency planning.
“With this dataset, humanitarian actors can identify which routes are likely to remain accessible under extreme weather or seasonal disruptions,” says Prof. Alexander Zipf, HeiGIT Scientific Director. “It provides a missing piece for navigation in regions where reliable road information is scarce.”
Beyond routing, the dataset also links improvements in road quality to human development. HeiGIT’s analysis reveals pronounced disparities: Europe and North America’s roads are more than 96% paved, while Sub-Saharan Africa averages only 63%, with the infrastructure deficit concentrated primarily in rural areas. Regions with a higher share of paved roads and a faster pace of new paving since 2020 tend to achieve higher Human Development Index levels, highlighting infrastructure as a dynamic measure of socioeconomic progress. Such data can support governments, development organizations, and researchers in assessing where strategic investment in road infrastructure would strengthen connectivity and resilience.
“Unlike traditional indicators such as nighttime lights, road networks reveal development at the local-level, showing how change unfolds on the ground.” says research lead Dr. Sukanya Randhawa. “By linking satellite imagery to patterns of economic development, we turn infrastructure change into a measurable signal of both progress and vulnerability.”
The dataset’s multi-temporal design captures shifts in road conditions as new infrastructure is built or existing roads deteriorate. This enables applications from global monitoring to local decision-making. At the planetary scale, it provides a framework for tracking development; at the national level, it supports assessments of network resilience and economic vulnerability. Case studies in Ghana and Pakistan highlight applications in urban equity analysis, climate resilience planning, and humanitarian response coordination.
By transforming road network from a static inventory into a dynamic indicator of change, the dataset provides a globally consistent foundation for informed routing and accessibility planning, while supporting the monitoring of progress toward the UN Sustainable Development Goals (SDGs).
The dataset is openly available via the Humanitarian Data Exchange — the open data platform operated by the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) — and will be updated annually. The underlying methodology and research is available as a preprint.
The research and data publication were supported by the Klaus Tschira Stiftung, whose funding enables HeiGIT to advance open geospatial technology for climate action and humanitarian aid.
Image on the right: © sig_3co, Panoramax; Figure on the left illustrates the workflow and challenges of extracting road attributes from 3-4m PlanetScope imagery.
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HeiGIT
HeiGIT (Heidelberg Institute for Geoinformation Technology) performs applied research relating to geographic information. Results and data are made freely available for the benefit of the environment and society through stakeholders such as researchers and aid organizations & associations.
The core foci of HeiGIT range from supporting humanitarian missions with customized geodata, to developing intelligent routing solutions for disasters or heatwaves. HeiGIT also implements innovative methods from the fields of spatial data processing and machine learning to analyze, enrich, and visualize geodata (e.g. OpenStreetMap data), as well as offering practical data analysis for climate protection initiatives.
HeiGIT gGmbH was founded in 2019 as an affiliated institute of Heidelberg University, with core funding from the Klaus Tschira Foundation.
Klaus Tschira Stiftung
The German foundation Klaus Tschira Stiftung supports natural sciences, mathematics and computer science and the appreciation of these subjects. It was founded in 1995 by physicist and SAP co-founder Klaus Tschira (1940–2015) by private means. Its three priorities are: education, research and science communication. This commitment begins in kindergarten and continues in schools, universities and research institutions throughout Germany. The foundation advocates the dialogue between science and society. Further information (in German) at: www.klaus-tschira-stiftung.de
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable