A Large Crowdsourced Street View Dataset for Mapping Road Surface Types in Africa

Identifying road surface types is crucial for road maintenance and socio-economic development. Crowdsourced street view data, with global coverage and free access, is a valuable source for this task. However, deep learning models typically require large, manually annotated datasets, which are lacking for road surface classification. To address this, three experts initially labeled 200,000 images from the Mapillary crowdsourced street view dataset, categorizing them into "Paved," "Unpaved," and "Unknown" road types through a voting mechanism. Five deep learning models were then trained on this expert-labeled dataset to assess the usefulness of the labels for further analysis. The results show that all models achieved accuracy, precision, recall, and F1 scores above 0.90, with the Swin Transformer model reaching the highest accuracy of 0.924. When applied to six 10 × 10 km sub-regions in Africa, the best model exceeded 0.95 in most performance metrics. These findings demonstrate the potential of this dataset to enhance road surface identification and support infrastructure improvement in Africa.