Paved or unpaved? A Deep Learning derived Road Surface Global Dataset from Mapillary Street-View Imagery

TL;DR

This paper introduces a global road surface dataset derived from 105 million Mapillary street-view images using advanced deep learning techniques, providing valuable insights for urban planning and sustainable development.

Contribution

It presents a novel hybrid deep learning approach combining SWIN-Transformer and CLIP-based methods to classify road surfaces and integrates this data with OpenStreetMap geometries.

Findings

Expanded global road surface data by over 3 million km, covering 36% of the world's roads.

Achieved high model accuracy with F1 scores between 91-97% for paved roads.

Identified significant gaps in paved road coverage in Africa and Asia.

Abstract

We have released an open dataset with global coverage on road surface characteristics (paved or unpaved) derived utilising 105 million images from the world's largest crowdsourcing-based street view platform, Mapillary, leveraging state-of-the-art geospatial AI methods. We propose a hybrid deep learning approach which combines SWIN-Transformer based road surface prediction and CLIP-and-DL segmentation based thresholding for filtering of bad quality images. The road surface prediction results have been matched and integrated with OpenStreetMap (OSM) road geometries. This study provides global data insights derived from maps and statistics about spatial distribution of Mapillary coverage and road pavedness on a continent and countries scale, with rural and urban distinction. This dataset expands the availability of global road surface information by over 3 million kilometers, now representing approximately 36% of the total length of the global road network. Most regions showed moderate to high paved road coverage (60-80%), but significant gaps were noted in specific areas of Africa and Asia. Urban areas tend to have near-complete paved coverage, while rural regions display more variability. Model validation against OSM surface data achieved strong performance, with F1 scores for paved roads between 91-97% across continents. Taking forward the work of Mapillary and their contributors and enrichment of OSM road attributes, our work provides valuable insights for applications in urban planning, disaster routing, logistics optimisation and addresses various Sustainable Development Goals (SDGS): especially SDGs 1 (No poverty), 3 (Good health and well-being), 8 (Decent work and economic growth), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable cities and communities), 12 (Responsible consumption and production), and 13 (Climate action).