Growth in the population and road transportation for any region can create a higher demand for better road conditions and the less safe road conditions can be a great bottleneck for the growth of the nation. Hence most of the progressive nations like India,builds better infrastructure for the road transportations.Nevertheless,the higher populations in countries like India,demand greater maintenance of the roads and for a gigantic demographic, the maintenance work can be very tedious. Also,the damage in the road surfaces can also lead to the increasing rate of the accidents. Henceforth,the demand of the road maintenance is always increasing.Nevertheless, the existing road maintenanceauthorities deploy a manual process for identification of the repair needs, which is naturally highly time consuming. Thus, this work identifies the demand for automation of the road condition monitoring system and identifies the defects based on three classes as cracks, patch works and potholes on the road surface. The work deploys a novel model for parametric extraction, in order to segregate the defect types. The segregation of the defect types can be highly challenging due to the nature of the data,which clearly hints to solve the problem using unsupervised methods. Thus, this work also deploys a pragmatic clustering method using a decisive factor,which is again generated from the extracted features of parameters. The work demonstrates nearly 96% accuracy on the benchmarked dataset with sophistication on the complexity of the model.

With the increase in the road transportation system the safety concerns for the road travels are also increasing. In order to ensure the road safety, various government and non-government efforts are visible to maintain the road quality and transport network system. The maintenance of the road condition is in the verse of getting automated for the quick identification of potholes, cracks and patch works and repair. The automation process is taking place in majority of the counties with the help of ICT enabled frameworks and devices. The primary device used for the purpose is the geo location enabled image capture devices. Regardless to mention the image capture process is always prune to noises and must be removed for better further analysis. Also, the spatial data is collected from the road networks are also prune to various error such as missing values or outliers due to the induced noises in the capture devices. Hence, the demand of the current research is to purpose a complete solution for the noise identification and removal from the spatial road network data for making the automation process highly successful and highly accurate. In the recent time, many parallel research attempts are observed, which resulted into solving the problem of noise reduction in all aspects of spatial data. Nevertheless, all the parallel research outcomes have failed to provide a single solution for all the noise issues. Henceforth, this work proposes three novel algorithms to solve spatial image noise problem using the adaptive moment filtration, missing value noise from the spatial data using adaptive logistic analysis and finally, the outlier noise removal from the same spatial data using corrective logistic machine learning method. The outcome of this work is nearly 70% accuracy in image noise reduction, 90% accuracy for missing value and outlier removal. The work also justifies the information loss reduction by nearly 50%. The final outcome of the work is to ensure higher accuracy for road maintenance automation.

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