Automated CAD File Generation from Aerial Data Using Machine Learning
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Solution Overview
Problem
Existing methods for creating computer-aided design (CAD) files are time-consuming, labor-intensive, and expensive, and they require significant computing resources for generation and viewing.
Innovation Solution
The use of machine learning, image analytics, and computer vision to automatically define CAD files by processing aerial data, identifying shapes, categorizing points, and generating polyline boundaries to create 2D and 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manual drafting and CAD methods are used to create CAD files, then detailed and accurate design models can be produced, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary actions by automatically classifying data points into categories and identifying shapes before generating the final CAD file. This preliminary classification and shape identification work is done automatically using machine learning models, eliminating the need for manual drafting while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary machine learning-based processing layer between raw aerial data and the final CAD output. This intermediary system automatically classifies data points, identifies shapes, and generates polyline boundaries, serving as a mediator that transforms raw data into accurate CAD files without manual intervention.
2Manufacturing precision
If detailed measurements and manual CAD processes are used, then accurate 2D and 3D models can be generated, but significant computing resources are required for generation and viewing
Solution Approach 1:
The patent segments the CAD generation process into distinct automated stages: data point classification, shape identification, and polyline boundary generation. Each stage is handled by specialized machine learning models that process only necessary data, reducing overall computing resource consumption while maintaining model accuracy.
Solution Approach 2:
The system performs self-service by automatically classifying data points and identifying shapes without requiring manual measurement or intervention. The machine learning models autonomously process the aerial data and generate accurate CAD representations, eliminating the need for resource-intensive manual processes.
3Productivity
If automated machine learning methods are used to define CAD files, then time and cost are reduced, but the complexity of the processing system increases
Solution Approach 1:
The patent employs universal machine learning models that can handle multiple tasks: classifying data points into various categories, identifying different shapes, and generating polyline boundaries. These multi-functional models reduce the need for separate specialized systems, managing complexity while maintaining high productivity.
Solution Approach 2:
The system manages complexity by changing parameters dynamically - adjusting classification categories, shape identification thresholds, and polyline generation criteria based on the specific aerial data being processed. This parameter-based approach allows the same system architecture to handle diverse CAD generation tasks without increasing structural complexity.
Data Source
AI summary
A non-transitory processor-readable medium includes code to cause a processor to receive aerial data having a plurality of points arranged in a pattern. An indication associated with each point is provided as an input to a machine learning model to classify each point into a category from a plurality of categories. For each point, a set of points (1) adjacent to that point and (2) having a common category is identified to define a shape from a plurality of shapes. A polyline boundary of each shape is defined by analyzing with respect to a criterion, a position of each point associated with a border of that shape relative to at least one other point. A layer for each category including each shape associated with that category is defined and a computer-aided design file is generated using the polyline boundary of each shape and the layer for each category.