Automated Semantic Annotation for Digital Building Models
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Solution Overview
Problem
Conventional building models, such as paper blueprints or PDF files with CAD designs, often lack strict semantics and consistent semantic annotations, making it difficult for machines to capture and process design data, resulting in high manual complexity for deriving simulation models.
Innovation Solution
A method and apparatus for generating a digital building model by reading an initial building model, extracting creator-specific information, loading a creator-specific object pattern library, and using pattern recognition to correlate object patterns with building elements, thereby reducing complexity and automating the annotation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional building models (paper blueprints, PDF files) are used, then the models are widely available and easy to obtain, but they lack strict semantics and consistent semantic annotations making machine processing difficult
Solution Approach 1:
The patent introduces an intermediary system consisting of pattern recognition algorithms and object pattern libraries that mediate between conventional building models and machine processing requirements. This intermediary layer automatically extracts and annotates semantic information from unstructured blueprints, converting them into machine-readable formats without requiring manual digitization of each element.
Solution Approach 2:
The patent replaces manual mechanical processes of semantic annotation with automated optical recognition and pattern matching systems. Instead of manually interpreting and annotating building elements in blueprints, the system uses automated image processing, pattern recognition, and machine learning algorithms to extract semantic information, thereby reducing manual complexity while preserving semantic integrity.
2Loss of information
If manual processing methods are used for conventional building models, then semantic annotations can be added, but the process requires high manual complexity and time
Solution Approach 1:
The system enables self-service automated annotation where the building model processing system automatically identifies, classifies, and annotates building elements without human intervention. The pattern recognition system independently matches objects in blueprints against stored object patterns, automatically generating semantic annotations for walls, doors, windows, and other building elements, thereby eliminating manual processing time while maintaining annotation completeness.
Solution Approach 2:
The patent implements preliminary action by pre-storing comprehensive object patterns and semantic definitions in databases before processing begins. The system pre-loads libraries of building element patterns, annotation rules, and semantic relationships, enabling rapid automated annotation during actual processing without requiring manual setup or interpretation for each new blueprint, thus dramatically reducing processing time.
3Measurement precision
If comprehensive object pattern libraries are created for all possible creators, then pattern recognition accuracy improves, but the library size and management complexity increases
Solution Approach 1:
The patent implements universality by designing a multi-functional object pattern library system that can handle multiple creators and building standards through a unified framework. The system uses universal pattern matching algorithms and standardized data structures that can accommodate diverse building elements from different sources, allowing the same library infrastructure to serve multiple purposes and creators without requiring separate management systems for each.
Solution Approach 2:
The patent applies segmentation by organizing the comprehensive object pattern library into modular, creator-specific sub-libraries or modules. Instead of managing one monolithic library, the system divides the pattern collection into manageable segments organized by creator, building type, or element category, allowing selective loading and processing of only relevant patterns for each specific task, thereby reducing management complexity while maintaining overall accuracy.
Data Source
AI summary
An initial building model is read in and a creator indication about the creator thereof is extracted therefrom. The creator indication is taken as a basis for loading a creator-specific object pattern library in which a respective object pattern has an assigned building element data record. The respective object pattern is correlated with objects of the initial building model by a pattern recognition method. At the same time, an object correlating with the respective object pattern is assigned the building element data record assigned to this object pattern as an annotation. The initial building model and the assigned annotations are then taken as a basis for generating and outputting an annotated digital building model.