This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The United Nations considers well-developed and safe roads an important part of infrastructure in its Sustainable Development Goals. Until now, however, there has been no benchmark for the condition and state of road networks worldwide.
Researchers at the Institute of Geography at Heidelberg University and HeiGIT (Heidelberg Institute of Geoinformation Technology) have addressed this gap by using artificial intelligence and satellite imagery to create an open-access dataset that maps and classifies more than 9 million kilometers of roads worldwide. The dataset also captures changes in road conditions over time, providing valuable information for humanitarian applications and serving as an important indicator for assessing socioeconomic development, particularly in data-scarce regions. The dataset is based on high-resolution images of Earth's surface captured by the PlanetScope satellites between 2020 and 2024.
Using deep learning, researchers in Heidelberg led by Prof. Alexander Zipf were able to identify approximately 9.2 million kilometers (5.7 million miles) of major thoroughfares and classify them based on their pavedness and width. "Our model is about 20 percentage points more accurate than previously used datasets and makes it possible to track changes in road infrastructure over periods of several years," explains Dr.
Sukanya Randhawa, who leads the "GeoAI for Good" projects at HeiGIT. In particular, this makes it possible to draw conclusions about how passable a road is under changing conditions—such as during extreme weather events. To this end, the researchers have converted the data into a Humanitarian Passability Matrix, which can be used to plan humanitarian operations.
The work is published in the journal Nature Communications. Road pavedness can also provide insight into the level of development of individual countries or regions. As analyses from 2020 to 2024 show, the degree of pavedness is directly linked to the level of development and development potential.
For instance, countries with a relatively high proportion of paved roads rank higher on the United Nations' Human Development Index than countries where major roadways are unpaved and road-building projects are progressing more slowly. "Road infrastructure can therefore serve as an indicator of socioeconomic progress at both the global and local levels, particularly in regions where traditional development indicators, such as nighttime light data, reach their limits," Randhawa emphasizes. This is demonstrated by a case study from Ghana, among others.
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