Machine Learning Ground Control Point Detection
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Solution Overview
Problem
Traditional methods for determining ground control points are labor-intensive and do not scale well, requiring manual efforts and significant resources, which limits their accuracy and update frequency, especially in applications like autonomous driving that demand centimeter-level map data.
Innovation Solution
A computer-implemented method using machine learning to automatically identify ground control points from image data by selecting features that meet specific properties, training a machine learning model with labeled ground truth images, and predicting pixel locations in input images, enabling scalable and accurate ground control point determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual ground surveyors are deployed to determine ground control points, then measurement precision can be maintained, but productivity is significantly reduced and the process does not scale well
Solution Approach 1:
The patent replaces the mechanical manual surveying system with an automated computer vision system using machine learning models. The system processes satellite or aerial images to automatically identify and classify ground control points, eliminating the need for physical deployment of surveyors while maintaining measurement precision through algorithmic detection and classification.
Solution Approach 2:
The patent creates a digital copy of the ground surveying process through machine learning models trained on labeled image data. The models learn from annotated training images containing ground truth labels and replicate the surveyor's ability to identify ground control points, enabling automated processing of large numbers of images without additional manual resources.
2Quantity of substance
If manual resources are limited, then resource allocation is optimized, but the ability to update and maintain ground control points deteriorates
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically processes images, identifies ground control points, and updates digital maps without requiring continuous manual intervention. Once trained, the system can independently process new images and generate updates, maintaining reliability and update frequency while minimizing ongoing manual resource requirements.
Solution Approach 2:
The patent performs preliminary training of machine learning models using extensively labeled ground truth images before deployment. This preliminary action creates a robust model that can handle various scenarios, reducing the need for frequent manual retraining or adjustments and ensuring reliable performance with limited ongoing manual resources.
3Device complexity
If traditional manual methods are used, then implementation simplicity is maintained, but adaptability to high-definition map requirements deteriorates
Solution Approach 1:
The patent segments the ground control point determination process into distinct automated stages: image acquisition, preprocessing, machine learning inference, and result validation. This segmentation allows each stage to be optimized independently while maintaining overall system manageability, enabling the system to meet high-definition map requirements without overwhelming complexity.
Solution Approach 2:
The patent creates a universal machine learning system that can process various types of images (satellite, aerial, drone) and identify multiple types of ground control points (road intersections, building corners, landmarks). This multi-functional capability allows the system to adapt to different high-definition map requirements while using a single integrated platform.
Data Source
AI summary
An approach is provided for determining a ground control point from image data using machine learning. The approach, for example, involves selecting an feature based determining that the feature meets one or more properties for classification as a machine learnable feature. The approach also involves retrieving a plurality of ground truth images depicting the feature. The plurality of ground truth images is labeled with known pixel location data of the feature as respectively depicted in each of the plurality of ground truth images. The approach further involves training a machine learning model using the plurality of ground truth images to identify predicted pixel location data of the ground control point as depicted in an input image.