Dataset for Road Roughness Assessment Using Image Classification Techniques and Deep Learning Models: A Case Study on Bangladeshi National Highways

Road quality assessment is a crucial task for maintaining transportation infrastructure, but it can be challenging and resource-intensive. Recent advances in remote sensing and machine learning have opened up new possibilities for assessing road quality using satellite images. This proposes a comprehensive dataset of approximately 45 k road images, which have been classified into five classes based on road quality. It contains N8, N102, N104, N502, N702, N704, N707, N803, N805, N806, N808, national highways of Bangladesh road images. The dataset has been used to train six deep learning models, including ResNet50, ResNet152, VGG19, DenseNet169, MobileNet V2, and SqueezeNet, on this dataset to identify the roughness levels of the road surfaces. The best accuracy of 82% was obtained from ResNet50. Our analysis shows that ResNet50 performs well on large dataset with noisy and unclear images due to its unique architecture that allows for better gradient propagation during training. This paper also analyzed the relationship between roughness levels and other factors such as traffic volume and road type. The findings of this study demonstrate the potential of satellite-based road quality monitoring for improving transportation infrastructure management and supporting economic development.