We propose a comprehensive AI-based solution for monitoring street markings that incorporates the following components:
Regular Data Collection:
Drones: Utilize high-resolution cameras mounted on drones to capture detailed images of street markings. Data collection will occur biannually (spring and autumn) to ensure up-to-date information on the condition of markings.
AI Analysis:
Machine Learning Models: Develop and deploy machine learning algorithms to analyze the collected imagery. The AI system will classify the condition of pavement markings into predefined wear classes (e.g., Class A: Excellent, Class B: Good, Class C: Poor).
Volume Calculation:
Wear Class Estimation: Calculate the volume of markings required for restoration based on the identified wear class. This will involve determining the extent of surface area that needs reapplication or repair.
Work Order Creation:
ArcGIS Integration: Automatically generate work orders and maintenance schedules within the ArcGIS environment. The system will integrate directly with existing GIS infrastructure to streamline task management.
Data Updating:
Spatial Data Management: Continuously update the spatial data in ArcGIS to reflect current conditions, including the identification of new markings and removal of outdated or missing ones.
