Automated Satellite Image-Based Parking Lot Detection System Development

The client faces challenges in quickly and accurately detecting parking lot locations across large geographic areas using satellite images, which hampers efficient parking management and resource allocation.

A mid-sized automotive service provider seeking to optimize parking lot management through advanced satellite and image recognition technologies.

• Develop a system capable of recognizing parking lots from satellite images with at least 85% accuracy.
• Implement a platform that allows easy deployment across various operating systems using a single codebase.
• Enable in-browser detection leveraging local computational resources to facilitate rapid analysis.
• Support fast system training and model updates to adapt to new geographic areas and use cases.
• Deliver exportable recognition results to integrate with existing management workflows.

• Satellite image preprocessing and enhancement for improved feature extraction.
• Deep learning model built with supervised training methods to identify parking lot features.
• In-browser implementation to enable local resource utilization and cross-platform compatibility.
• Exportable recognition results for integration into broader management systems.
• Rapid training workflows for incorporating new geographic regions or data sets.

• Satellite imagery sources such as Google Maps or equivalent providers
• Existing parking lot management or analytics systems for data exchange

• Achieve at least 85% recognition accuracy in diverse geographic regions
• Support deployment across multiple platforms with minimal configuration
• Ensure fast training cycles allowing new use case adaptation within days
• Maintain system responsiveness for in-browser detection and processing

The project aims to significantly enhance parking lot management efficiency by enabling accurate satellite-based recognition at scale, reducing manual detection efforts, and accelerating resource deployment. Expected outcomes include recognition accuracy exceeding 85%, rapid system training, and cross-platform operability, leading to improved operational decision-making and resource optimization.