Roof Style Classification Using 3D Point Clouds
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Solution Overview
Problem
Current roof modeling techniques using aerial imagery are fragile due to lack of height information and are prone to errors from occlusion and blur, limiting the recognition and classification of roof styles, especially in partially occluded buildings.
Innovation Solution
The method involves receiving and processing three-dimensional point cloud data using machine learning algorithms to classify roof styles by identifying semantic types and analyzing the distribution of these types, enabling the recognition of multiple roof styles without requiring complete and specific source data, and is robust against data degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If aerial imagery is used for building modeling, then the modeling process can be performed with available image data, but the detection is highly dependent on circumstances and easily fails due to occlusion or blur
Solution Approach 1:
The patent transitions from two-dimensional aerial imagery to three-dimensional point cloud data, adding the depth dimension to capture complete building geometry including occluded regions. This dimensional change enables reliable detection of building structures that are not visible in top-down aerial views alone.
2Ease of manufacture
If aerial imagery is used for building modeling, then the process can proceed with existing data sources, but height information is lacking making the modeling fragile
Solution Approach 1:
The patent incorporates LiDAR-based point cloud data that provides complete three-dimensional information including height, replacing the two-dimensional aerial imagery that lacks vertical dimension. This enables accurate reconstruction of building geometry with proper height information.
3Productivity
If traditional geometry detection methods are used, then simple roof structures can be classified, but complex roof structures and partially occluded buildings cannot be recognized
Solution Approach 1:
The patent changes the feature representation parameters from simple geometric properties to distributional characteristics of semantic point types. By analyzing the spatial distribution patterns of different point types (e.g., ridge points, slope points, corner points), the system can distinguish complex roof styles that share similar local geometries but differ in overall structural patterns.
Solution Approach 2:
The patent transitions from analyzing only visible surface geometry to utilizing complete three-dimensional point cloud data that includes occluded regions. This enables the detection of roof structures that extend beyond the visible boundaries in aerial imagery, allowing classification of complex and partially occluded buildings.
4Measurement precision
If complete and specific source data is required for roof geometry analysis, then accurate roof style classification can be achieved, but the method is not robust against data degradation
Solution Approach 1:
The patent demonstrates that complete and specific source data is not necessary for accurate roof style classification. By analyzing the distribution patterns of semantic point types, the system can achieve high classification accuracy even with partial or degraded data, making the method robust against data quality issues.
Data Source
AI summary
Systems, apparatuses, and methods are provided for three-dimensional modeling of building roofs using three-dimensional point cloud data. Point cloud data of a roof of a building is received, and roof data points are selected or extracted from the point cloud data. Semantic type classifications are calculated for each selected roof data point. Roof styles are determined from the semantic type classifications, and a synthetic model of the roof and building is rendered based on the determined roof style.