AI maps the paved and unpaved fate of 9.2 million kilometers of road

Every economy on Earth runs on its roads, yet for nearly half of the world’s most important thoroughfares, nobody could say with confidence whether they were paved or dirt. A new study published in Nature Communications has closed that gap with a deep learning analysis of satellite imagery, classifying the surface type and width of 9.2 million kilometers of the planet’s critical arterial roads for both 2020 and 2024. The team, led by researchers at the Heidelberg Institute of Geoinformation Technology and Heidelberg University, mapped 95.5 percent of this network, nearly half of which had previously lacked any surface classification at all. The result is not just a map but a dynamic, multi-year record of how infrastructure investment is reshaping the globe, and it reveals stark divides between rich and poor regions, between cities and countryside, and between nations whose paved networks are resilient and those where a single washed-out dirt road can sever an entire economy.

The technical core of the project is a fine-tuned Mask2Former semantic segmentation model, a transformer-based architecture designed to classify imagery pixel by pixel. The researchers trained it on manually labeled PlanetScope satellite imagery, which offers a resolution of three to four meters, and then applied it to roughly 13.9 million road locations worldwide, using OpenStreetMap geometries as the spatial scaffold. Each road segment was processed through the segmentation model and a chain of geospatial post-processing steps, including polygonization, directional filtering, and integration with existing map data, to assign a final surface classification. Validated against an independently constructed, human-verified dataset of 2,628 road segments annotated on sub-meter imagery, the model achieved an overall accuracy of 89.2 percent, with precision of 97.7 percent and recall of 90.4 percent for paved roads. That performance matters because the de facto alternative, OpenStreetMap’s crowd-sourced surface tags, achieved only 64.7 percent accuracy on the same ground truth, and covered just 30 to 40 percent of the global network, often with outdated labels.

The accuracy gap between the machine and the crowd tells a story about the pace of change. In fast-developing regions, roads that were dirt a few years ago are now asphalt, but volunteer mappers have not always kept up, leaving tags that describe a road as it was rather than as it is. The model, drawing on fresh satellite passes, correctly reclassified many of these newly paved roads. The researchers also compared their approach to an earlier effort based on street-level imagery from Mapillary, which had raised global surface coverage only marginally, from 33 to 36 percent, because open street-level photos remain sparse and temporally inconsistent. The satellite-based method, by contrast, delivers near-complete coverage of the arterial network and can be repeated on a regular cadence, turning a static inventory into a monitoring system.

The global picture that emerges is one of dramatic inequality. Europe and Central Asia and North America show near-complete paved networks, at 97.4 and 96.9 percent respectively. Sub-Saharan Africa averages just 63.1 percent, with a variance of plus or minus 22.6 percentage points that signals deep heterogeneity between and within countries. At the national extremes, Qatar and Barbados approach fully paved networks, while the Central African Republic sits at 9.3 percent, the Democratic Republic of Congo at 19.0 percent, and South Sudan at 25.3 percent. Perhaps the most striking pattern is the urban-rural dichotomy: urban roads are more than 93 percent paved in every region, but rural pavedness in Sub-Saharan Africa lags at 61.4 percent against 97.2 percent in Europe and Central Asia. The infrastructure deficit, in other words, is overwhelmingly a rural phenomenon, even in the poorest countries.

Because the dataset spans two time points, the researchers could measure change, not just status. Between 2020 and 2024, pavedness increased almost everywhere, but the fastest relative gains were concentrated in lower- and lower-middle-income regions across South America, Africa, South Asia, and Southeast Asia, pinpointing the planet’s hotspots of road development. The team normalized this change by each country’s remaining paving potential, so that nations starting from mostly unpaved networks could be compared fairly with those finishing the job. Even after filtering out countries with little unpaved road left and controlling for baseline pavedness, the normalized rate of improvement correlated strongly with the Subnational Human Development Index, with a partial correlation of 0.54. The static share of paved roads correlated even more strongly, at 0.73 for total networks and 0.74 for rural roads, while urban correlations were much weaker, at 0.54, precisely because urban networks are near-saturated everywhere. Rural road quality, it turns out, is one of the most sensitive satellite-visible indicators of a country’s development stage.

This positions road surface data as a rival to the most famous satellite proxy for development: nighttime lights. Radiance-based measures from instruments like VIIRS have coarse spatial resolution of roughly 500 to 750 meters and only an indirect relationship to the physical infrastructure that enables growth. Pavedness, by contrast, is a direct, physically grounded signal of investment and accessibility, resolved at three to four meters. The authors are careful to frame the relationship as correlation rather than causation, but the implication for policy is substantial: governments and international bodies could track infrastructure progress in near real time, bypassing the multi-year lags of official statistics, and assess progress toward Sustainable Development Goals with data that updates as fast as the roads themselves.

The study goes beyond correlation with a functional, systems-level analysis of network fragility. Using graph-based connectivity modeling, the researchers simulated removing all unpaved roads from each country’s network and calculated an unreachable ratio: the share of trips between major urban centers that would become impossible. In North America, Europe, and East Asia, the ratio is near zero, reflecting mature paved networks with redundant paths between economic hubs. But across much of Sub-Saharan Africa, parts of South America, and Southeast Asia, the ratio climbs sharply, revealing nations where unpaved roads are not peripheral conveniences but the essential connective tissue of the national economy. The metric measures structural vulnerability rather than day-to-day conditions, but it offers policymakers a concrete benchmark: these are the specific links whose paving would most strengthen national integration and trade resilience.

At the local scale, the dataset becomes a lens on governance and equity. In Accra, Ghana, combining the satellite-derived data with street-view classifications exposed neighborhood-level infrastructure gaps in areas such as Redco and Macarthy Hill, dense with unpaved roads compared to better-served central districts. It also revealed enclaves like Sakumono Estate, where private developers paved internal roads as a market strategy while surrounding communities remained unpaved. Nationally, Ghana’s highway-class roads were 84.6 percent paved, urban roads 44.9 percent, and feeder roads just 5 percent, a hierarchy that mirrors the split mandates of the Ghana Highways Authority, the Department of Urban Roads, and the Department of Feeder Roads, a fragmented governance structure that the country’s 2023 National Roads Authority Bill explicitly targets. In Pakistan, the team overlaid their data on flood extents from the Copernicus Emergency Management Service in Punjab, deriving road widths and a Humanitarian Passability Score that distinguishes primary supply corridors from high-risk chokepoints, intelligence designed to shorten lead times in disaster response and guide climate adaptation investment.

The authors are candid about limitations. The three-to-four-meter imagery resolution limits the precision of road width estimates and prevents resolving individual lanes, dense forest canopy can obscure roads and push classifications toward unknown, and some apparent decreases in pavedness may reflect cloud cover, seasonal moisture, or sensor differences rather than real degradation. The team also stresses that the findings are not a universal argument for paving everything: unpaved roads remain cost-effective, support rural livelihoods, and can carry lower environmental footprints when well maintained. The strategic question, they argue, is not whether all roads should be sealed but where upgrading delivers the greatest gains in resilience and connectivity. With the full dataset openly available through the Humanitarian Data Exchange and analysis code published, the study offers the scientific community, humanitarian agencies, and governments something they have never had before: a current, consistent, and repeatable measurement of the physical surface of the world’s roads, and with it, a new way of watching development happen from orbit.

Subject of Research: Global mapping of road surface pavedness and width using deep learning on satellite imagery

Article Title: The changing surface of the world’s roads

Article References: Randhawa, S., Randhawa, G., Langer, C., Andorful, F., Herfort, B., Kwakye, D., Olchik, O., Lautenbach, S., & Zipf, A. (2026). The changing surface of the world’s roads. Nature Communications, 17(1), Article 9345. https://doi.org/10.1038/s41467-026-76234-8

Image Credits: AI Generated

DOI: 10.1038/s41467-026-76234-8

Keywords: road infrastructure, deep learning, satellite imagery, PlanetScope, human development, OpenStreetMap, climate resilience, rural access, semantic segmentation, humanitarian logistics, Sub-Saharan Africa, sustainable development