Why AI-Powered Roadway Inventories Are the Future of Urban Planning

Discover how AI and geospatial data are supplementing manual road surveys. Urban SDK enables faster, accurate roadway inventories for smart city planning.

Urban SDK is leading the next evolution of roadway inventory through artificial intelligence and geospatial data. Traditionally, transportation departments relied on field crews equipped with measuring wheels, clipboards, and vehicles to gather data on lane widths, signs, and pavement markings. While this method offered accuracy, it was slow, costly, and often left cities with outdated datasets. Today, AI-powered roadway inventories are revolutionizing this approach by using computer vision and machine learning on satellite, aerial, or street-level imagery to automatically identify road features at scale.

With Urban SDK's Roadway Characteristics dataset, users can leverage AI and satellite imagery to efficiently survey roads, curbs, signals, signage, and more—reducing years of manual work to hours. The fusion of AI with geospatial intelligence (GeoAI) enables municipalities to capture and update road networks consistently, regardless of their complexity. In a California pilot, for example, AI systems mapped thousands of miles of sidewalk 45 times faster than traditional methods, demonstrating transformative time savings.

Traditional vs. AI-Powered Approaches

Manual GIS Surveying

Transportation agencies have long gathered roadway data through manual surveying. Crews walk or drive roadways, using measuring tools and notebooks to log data.

Although reliable, this method covers limited ground each year and often results in outdated information.

Mobile Mapping Systems

Some cities have adopted lidar-equipped or camera-mounted vehicles that scan roads while driving.

This enhances data capture but still requires considerable manual processing, such as reviewing video footage to identify features.

AI-Powered Automation

Urban SDK and similar platforms revolutionize this process. By processing aerial or satellite imagery through AI models, these tools automatically extract attributes like lane widths, surface types, pavement conditions, and signage.

The result is a scalable, repeatable, and current dataset that updates with new imagery.

Benefits of AI-Powered Roadway Inventories

Cost Savings

Automating road data collection slashes labor and equipment costs. One customer saved over $80,000 by replacing a traditional multi-year manual survey with an AI system.

Urban SDK's solution reduces the need for field crews and specialized vehicles, allowing even smartphones to aid data collection.

Improved Accuracy

AI systems ensure consistency by applying the same recognition logic across every road segment. They can identify subtle defects, faded markings, and missing signs that human surveyors might overlook.

Platforms like Vaisala's RoadAI perform inspections at highway speeds with accuracy comparable to traditional methods.

Time Efficiency

With AI, months of data collection can be compressed into weeks. In states like California and North Carolina, AI tools digitized entire road networks in record time.

Urban SDK allows departments to maintain up-to-date maps and eliminate long backlogs.

Enhanced Safety

Detailed inventories feed directly into safety models. Urban SDK's Collision Index uses AI-extracted roadway data to identify high-risk segments, allowing cities to act proactively.

Automated systems also flag missing or worn infrastructure faster, improving pedestrian and driver safety.

Smart City Integration

AI-generated road inventories are delivered as georeferenced GIS layers that integrate seamlessly with city systems.

Urban SDK's datasets work alongside traffic sensors, asset management platforms, and public dashboards, enabling real-time planning and transparency.

Key Use Cases

Asset Management and Planning

A complete inventory of road assets—from signals to sidewalks—is foundational for infrastructure planning. Urban SDK's platform enables cities to use validated data in order to schedule upgrades and align budgets with up-to-date asset maps.

Road Maintenance

By continuously analyzing new imagery, Urban SDK provides a quick way to identify road deterioration, potholes, and faded markings before they become hazards. Predictive maintenance planning becomes feasible and cost-effective.

Traffic Safety Analysis

Precise road geometry and signage data enhances crash modeling.

Urban SDK's Speed Limit Difference tool compares mapped speed limits with real driving speeds to identify enforcement gaps.

Mobility and Accessibility Planning

AI-derived maps show the locations of bike lanes, ADA ramps, sidewalks, and transit stops—critical for inclusive planning.

In one project, AI tools mapped 16 road features, including medians and turn lanes, enabling smarter route design and improved pedestrian safety.

Urban SDK's AI-Driven Platform

Urban SDK offers an advanced platform for automated roadway inventories. Its Roadway Characteristics product uses satellite imagery and AI to deliver comprehensive, 3D road geometry and facility data. Municipal leaders can view, analyze, and download this data via Urban SDK's dashboard.

This geospatial AI solution covers everything from lane counts to sidewalk availability, and it integrates directly into existing GIS platforms. Urban SDK's platform also incorporates live traffic and safety data, delivering a 360-degree view of urban mobility.

Urban SDK's slogan—"Own the road forward with geospatial AI"—captures this mission. Whether updating signage locations, analyzing speed trends, or planning equitable infrastructure investments, Urban SDK equips planners with data they can act on.

Conclusion

AI-powered roadway inventories represent the future of urban infrastructure planning. Urban SDK demonstrates how combining satellite imagery, AI, and GIS can deliver comprehensive, up-to-date, and actionable roadway data. This shift from manual to automated inventory enables faster maintenance, safer streets, and smarter planning. Cities that embrace this technology will be better equipped to serve their communities, cut costs, and build resilient, future-ready road networks.