Mapping National‐Scale Road Surface Types Using Multisource Open Data and Deep Learning Model.

Identifying road surface type (e.g., paved and unpaved) is crucial for pavement maintenance, transportation management, and road network accessibility research. Existing approaches relying on vehicle‐mounted devices or remote sensing data are have limitation for large‐scale road networks. This study proposes a novel approach to identify national‐scale road surface type using multiple open geospatial datasets and machine learning models. Specifically, 16 input variables were designed based on these datasets (including OpenStreetMap, GDP, population, building height, and land cover). Nigeria and Cameroon were selected as study areas. A substantial dataset, auto‐extracting road surface tags from OpenSreetMap, was used to train a model. The trained model predicted road surface types across the two study areas. Result indicated: (1) Most of the input variables positively impact the output variable, with "road class" being the most influential; (2) The proposed approach with deep learning model‐TabNet performs the best, with an overall accuracy above 85%; and (3) More than 83% of roads in the two African countries are unpaved, with paved roads concentrated in backbone roads and southern provinces. This approach has been validated and offers valuable insights for local authorities aiming to enhance road infrastructure. [ABSTRACT FROM AUTHOR]