A Comprehensive Study on Road Quality Measurement from High Resolution Satellite Imagery

Satellite imagery is a valuable data repository for detecting, quality measurement, and feature mapping. Among all the types of expenditure, preventive maintenance of the road network is the most economical and beneficial. Analyzing the existing architectures for road quality measurement using satellite imagery and comparing the multiple models, we have concluded that Deep Learning models work well rather than the Machine Learning model. To validate our hypothesis, we have carried out a comprehensive study on the existing papers covering available datasets, preprocessing techniques, segmentation, feature extraction, and classification methods. Among the learning models, SqueezeNet which is a compressed version of AlexNet has achieved the optimal performance in determining road class labels in the publicly annotated datasets. Moreover, in terms of road quality measurement, an ensemble approach combining Auto-encoder with LSTMs outperformed in holdout dataset.