Auto Classifying Roof Elements from Geo-Referenced Images
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Solution Overview
Problem
Existing software for estimating roofing projects using geo-referenced oblique images lacks the capability to automatically classify roof elements such as ridge lines, drip edges, and eaves, making it difficult to generate reports with cumulative lengths of these elements.
Innovation Solution
A computer system with instructions that classify line segments in geo-referenced images as predefined roof elements based on their relative position and orientation, using a methodology that involves defining roof sections, grouping end points by elevation, and calculating angles between segments to identify features like ridges, valleys, eaves, and rake lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement and classification of roof elements is performed, then accuracy of roof element identification can be maintained, but time consumption and labor costs increase significantly
Solution Approach 1:
The system enables automatic self-classification of roof elements by the computer itself through algorithmic processing of geo-referenced images. The computer automatically identifies and classifies line segments into roof elements (ridges, valleys, eaves, rakes) without requiring manual interpretation, thereby resolving the contradiction between measurement accuracy and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual measurement process with an automated computer-based system that uses geo-referenced images and algorithmic classification. This substitution eliminates manual labor while maintaining measurement precision through systematic processing of roof line segments.
2Productivity
If automated classification of roof elements is implemented, then productivity and speed of estimation increase, but system complexity increases
Solution Approach 1:
The patent segments the roof into discrete line segments that can be individually classified and processed. By dividing the complex roof structure into manageable segments (ridges, valleys, eaves, rakes), the system achieves automated classification without requiring overly complex processing, thereby improving productivity while controlling system complexity.
Solution Approach 2:
The system uses parameter-based classification of line segments based on their geometric properties and spatial relationships in geo-referenced images. By changing the approach from manual visual inspection to parameter-driven automated classification, the system increases productivity while managing complexity through structured data processing.
3Loss of information
If detailed classification of each roof element is performed, then completeness of measurement data improves, but measurement complexity and difficulty increase
Solution Approach 1:
The computer system automatically performs the difficult task of classifying each line segment into specific roof elements by itself, using algorithmic processing of geo-referenced images. This self-service approach ensures complete classification of all roof elements without requiring human expertise, thereby improving data completeness while reducing measurement difficulty.
Solution Approach 2:
The patent replaces the difficult manual classification process with automated computer-based image processing and pattern recognition. This substitution enables detailed classification of all roof elements systematically, ensuring complete measurement data while eliminating the complexity of manual detection and measurement.
Data Source
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AI summary
A set of instructions stored on at least one computer readable medium for running on a computer system. The set of instructions includes instructions for identifying line segments of a roof that is preferably displayed within a geo-referenced image, instructions for determining three-dimensional information of the line segments including position, orientation and length of the line segments preferably utilizing the geo-referenced image, and instructions for classifying, automatically, at least one of the line segments as one of a plurality of predefined roof elements utilizing at least one of the relative position and orientation of the line segments.