AI Road Maintenance Case Study: 90% CO2 Reduction
AI-Driven UAV Road Inspection Cutting CO2 Emissions by 90%: GreenBee Case Study
How Agmis partnered with state-owned AB Kelių priežiūra and UAV specialist Thrust to pioneer the GreenBee project, transforming Lithuania's road inspection practices across 21,000+ kilometers while dramatically reducing environmental impact.
AB Kelių priežiūra – Lithuania's National Road Maintenance Authority
AB Kelių priežiūra serves as Lithuania's state-owned road maintenance organization, responsible for preserving and monitoring the nation's extensive road network. With oversight of more than 21,000 kilometers of national roads, the organization plays a critical role in ensuring safe and reliable transportation infrastructure for millions of citizens. Their commitment to innovation and sustainability made them an ideal partner for exploring next-generation inspection technologies through the GreenBee initiative.
Traditional Road Inspection Methods Prove Unsustainable at Scale
Resource-Intensive Operations
Conventional road inspections required manned vehicles traveling extensively across the entire road network, consuming substantial time, fuel, and personnel resources.
Environmental Burden
Frequent inspection trips generated significant CO2 emissions, creating an environmental footprint inconsistent with modern sustainability goals.
Efficiency Limitations
Ground-based inspection methods struggled to cover vast distances quickly, leading to delays in identifying and addressing road defects.
Inconsistent Data Quality
Manual inspection processes were susceptible to human error and inconsistencies, particularly when assessing large areas under varying conditions.
Intelligent UAV Technology Combined with Advanced Computer Vision
Aerial Data Collection: Unmanned aerial vehicles equipped with advanced sensors and high-definition cameras capture comprehensive road infrastructure data from above, eliminating the need for ground vehicles.
AI-Powered Defect Detection: Advanced AI and Computer Vision technologies rapidly analyze captured imagery, accurately identifying defects and infrastructure issues with precision impossible through manual methods.
Reconnaissance-First Approach: UAVs efficiently cover large areas to pinpoint potential problem zones, enabling strategic planning of targeted manned inspection routes only where needed.
Optimized Resource Deployment: Teams are dispatched specifically to areas requiring prompt action based on AI-driven data analysis, maximizing efficiency and minimizing unnecessary travel.
Environmental Sustainability: By dramatically reducing reliance on manned inspection vehicles, the solution achieves a 90% reduction in CO2 emissions associated with road monitoring activities.
Scalable Infrastructure Coverage: The technology adapts seamlessly to road networks of any size, from regional roads to national highway systems spanning thousands of kilometers.
Environmental Impact
Reduction in CO2 Emissions
Transformative Impact on Road Maintenance Operations
Dramatic Environmental Improvement
The shift from ground-based to aerial inspection achieved a 90% reduction in CO2 emissions, setting a new benchmark for sustainable infrastructure management.
Enhanced Detection Accuracy
AI-powered analysis eliminates inconsistencies inherent in manual inspections, delivering faster and more precise identification of road defects.
Optimized Maintenance Efficiency
Targeted deployment based on AI insights ensures maintenance teams address genuine priorities rather than conducting routine sweeps of unaffected areas.
Future-Ready Framework
The GreenBee project establishes a scalable model for infrastructure inspection that can be replicated across road networks throughout Europe and beyond.
Strategic Reconnaissance Capability
By rapidly covering large areas and pinpointing areas of potential defects, UAVs and AI analysis will act as a reconnaissance service. This will allow us to efficiently plan manned inspection routes and to dispatch teams to the areas requiring prompt action.
Continental-Scale Potential
The length of the EU road network stretches for approximately 5 million kilometers. We estimate that the current road inspection framework requires driving 93.7 million kilometers and committing 3.1 million man-hours every year, with up to 37.5 thousand tons of CO2 emitted into the atmosphere. Employing UAVs for the task cut these emissions by 90%.
Precision Over Volume
Ground teams now operate with surgical precision, responding to verified issues rather than conducting speculative patrols across the entire network.
Sustainable Operations Model
The integration of UAV and AI technologies demonstrates that environmental responsibility and operational excellence can advance together.
Establishing New Standards for Infrastructure Management
For transportation authorities and infrastructure managers worldwide, maintaining road quality across vast networks presents an ongoing challenge. This AI-driven approach delivers transformative value through:
Massive Scale Efficiency
With EU road networks spanning 5 million kilometers, traditional inspection methods are increasingly untenable. UAV and AI technology offers the only viable path to comprehensive coverage without proportional resource increases.
Environmental Leadership
A 90% reduction in inspection-related emissions demonstrates that infrastructure maintenance can align with climate goals without compromising thoroughness or quality.
Data-Driven Decisions
Comprehensive aerial imagery combined with AI analysis empowers maintenance teams to prioritize interventions based on objective evidence rather than routine schedules.
Global Replication Potential
The GreenBee project serves as a proof of concept for transportation authorities worldwide, illustrating how technology can sustainably transform infrastructure management practices.