Building Footprint Analysis Using Deep Learning and Geometric Refinement
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Solution Overview
Problem
Current building footprint analysis techniques suffer from misalignment, inconsistency, inaccurate representation of building structures, lack of adaptability to structural changes, and the need for manual post-processing, leading to limited accuracy and reliability.
Innovation Solution
The application of deep learning algorithms and refinement techniques such as property shift, building shift and rotation, wall sliding, and building addition, replacement, and removal to analyze overhead imagery and contextual data, providing accurate alignment, consistency, and adaptability to structural changes without manual post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated building footprint analysis techniques are applied to overhead imagery, then scalability and cost-effectiveness are improved, but misalignment, inconsistency, and inaccurate representation of building structures occur
Solution Approach 1:
The patent replaces traditional mechanical/algorithmic image processing methods with deep learning-based semantic segmentation. The system uses neural networks trained on labeled building footprint data to automatically identify and delineate building structures in overhead imagery, substituting conventional computer vision algorithms with learned models that generalize better across diverse scenarios while maintaining high accuracy and scalability
Solution Approach 2:
The patent transforms building footprint analysis from a geometric measurement problem to a pixel classification problem by changing the fundamental parameters of the analysis. Instead of detecting edges and contours through traditional image processing parameters, the system uses deep learning to classify each pixel as building or non-building, fundamentally altering the analytical approach to achieve both accuracy and scalability
2Measurement precision
If deep learning algorithms are applied to overhead imagery to generate building outlines, then accuracy and adaptability are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models on extensive labeled datasets of building footprints before deployment. The system performs offline training and validation to establish robust models that can be directly applied to new imagery without requiring complex real-time adjustments, thereby reducing online computational complexity while maintaining high accuracy
Solution Approach 2:
The patent uses copying by training the deep learning model on copies of labeled building footprint data from various sources. The system learns from multiple examples of building structures across different regions and architectures, creating a generalized model that can accurately identify building outlines in new imagery without requiring complex case-specific processing
Data Source
AI summary
Methods, non-transitory computer readable media, and building analysis systems are disclosed that analyze an overhead image to generate a building outline for a building associated with a property represented by the overhead image. The overhead image is included in imagery data obtained from an overhead imagery server based on a received request comprising a geographic location for the property. The building outline is then shifted or rotated. Segment(s) of the building outline are slid to match identified wall(s) of the building. The building outline is then modified based on property feature(s) detected based on an application of one or more trained machine learning classifiers to the overhead image. At least a portion of the overhead image is output with a graphical overlay comprising the building outline via a user interface in response to the received request.