Cyclomedia's Road Surface Analysis: Fast and accurate analysis of every roadway lane

It makes perfect sense to have a computer inspect road surface damages, as 'on-site' inspections are completely subjective and take an incredible amount of time and money to perform. By training AI to detect road damages and their severity, we can move from a subjective scoring to an objective one, free from human error- this is exactly what Cyclomedia has accomplished with our RSA product.

To do this, we added the knowledge and years of experience belonging to the Arcadis road inspectors to our RSA algorithm. The algorithm was trained with the RSA data collected by the inspectors, resulting in an AI driven, single set of eyes which looks objectively at each individual road defect, year after year. The AI technology will be used to create a road inspection that is in line with the most common methodologies, such as ASTM 6433 –07 (Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys - American Society for Testing and Materials) and PASER (Pavement Surface Evaluation and Rating system ) in the US.

To build a successful multi-year pavement preservation plan, high-quality, multi-year datasets are needed. The RSA product can be produced after every capture done by Cyclomedia. And because the same algorithms will be scanning the same type of imagery from year to year, the data can be compared over time, providing the needed output to prove the pavement strategy is resulting in an overall improved pavement quality.The data will be transparent and can be shared with citizens to assure them that budgets are being spent in the most optimal way. 

By fixing roads that are just beginning to degrade, total pavement life is extended, thus maximizing the value of each dollar spent on the roads. Another important benefit of the pavement preservation approach is that it enables the preservation of four to ten times more streets than if resources were used to fixing the ‘bad' roads first, since the treatment cost to preserve ‘good' roads is substantially less. An overall increased PCI scoring based on high-quality data will prove the strategy is working.

With the power of machine learning, road inspections become faster and more accurate. By using historical data, the system learns to identify different types of road defects. This makes automated inspections possible, saving time and resources. Data analysis helps to understand the current road conditions and predict future issues, which is crucial for maintaining road safety.

The integration of digital technologies in road inspections revolutionizes the way we maintain our roads. Automated inspections using image recognition and data analysis lead to better road safety and more efficient use of resources. By focusing on road defects, severity, and extent, we can ensure that our road surfaces are always in top condition. This improves the quality of the roads and reduces the costs associated with traditional inspection methods. With AI, we move towards a future where road conditions are monitored and maintained seamlessly.