AI Overview
This article explains how AI can automate parking lot striping cost and effort estimation. It covers image analysis, data processing, and workflow automation. It shows how automation improves accuracy and speeds up operations. It highlights real world AI driven efficiency gains.
In the pavement maintenance industry, the quoting process has traditionally been manual and time-consuming. Estimators often have to physically visit sites or manually count stall lines from grainy satellite maps to generate a quote. We set out to streamline this workflow by building a solution that transforms a simple address into a detailed financial estimate using satellite imagery and Generative AI.
Here is an inside look at how we engineered this automated estimation pipeline.
Digital Reconnaissance: Acquiring the Visual Data
The first step in our workflow is automating the “site visit.” Instead of sending a truck, we utilize a combination of Geocoding and Static Maps APIs to retrieve high-resolution satellite imagery of the target location.
The system takes a list of target addresses and converts them into precise geographic coordinates. It then fetches satellite imagery, specifically excluding roadmap features to focus entirely on physical ground details like pavement and existing striping.
The Challenge of Resolution: One of the key technical hurdles was finding the “Goldilocks” zone for image clarity. We tested various zoom levels (ranging from 17 to 20).
- Too zoomed out (Level 17): We capture the whole property but lose the definition of individual parking lines.
- Too zoomed in: We get great detail but risk cropping out 30-50% of the lot, especially for vertically shaped properties.
- The Sweet Spot: We found that zoom levels 18 and 19 were optimal for the majority of locations, providing clear visibility of lines without significant cropping.
AI-Powered Analysis: The “Zero-Shot” Approach
Once we have the imagery, the next phase is extraction. We employed Gemini models to visually reason over the satellite images. In our initial implementation, we used a “zero-shot” approach—asking the raw model to identify and count specific markers directly from the image.
The model was tasked with identifying:
- Standard Parking Lines: Counting the typical 18ft stall lines.
- Specialty Markers: Identifying handicap symbols (blue/white stencils) and traffic flow arrows.
- Layout Configuration: distinguishing between single-row, high-density, or mixed-row configurations.
The Calculation Engine: Generating the Quote
The data extracted by the AI is then fed into a financial logic layer. This system maps the visual counts to a standard unit pricing reference.
For example, the system automatically calculates:
- Material Costs: Multiplying the line count by the unit price (e.g., standard lines vs. pricier handicap stencils).
- Gross Profit Projection: Subtracting estimated material costs from the total service price.
This turns a raw image into a structured financial summary, breaking down costs for materials and striping services instantly.
Achieving Commercial-Grade Accuracy with Fine-Tuning
While zero-shot inference demonstrates immediate feasibility, production-grade reliability is secured through Model Fine-Tuning. By specializing the model—specifically targeting architectures like Gemini 2.5 Flash—on a labelled dataset of pavement imagery, the system bridges the gap between general recognition and expert analysis. This targeted training eliminates the unpredictability often found in raw models, effectively curbing “hallucinations” and ensuring precise distinction between complex stencil configurations and faded, non-active lines.
