Home Façade Feature Detection Using CNN Image Quantification
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Solution Overview
Problem
Existing methods lack an efficient and automated way to detect and quantify exterior features of houses from remote sensing imagery, such as doors, windows, and façades, and to provide detailed geographic analysis of these features.
Innovation Solution
A system utilizing Convolutional Neural Networks (CNNs) to analyze street-level and aerial imagery, cropping images to focus on individual houses, and converting pixel data to terrestrial measurements for detailed feature detection and quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection and quantification of exterior features is performed, then measurement precision can be maintained, but productivity is significantly reduced
Solution Approach 1:
The patent replaces manual mechanical detection with an automated Machine Learned Model (Convolutional Neural Network) that processes street-level images to identify and quantify exterior features. This substitution maintains measurement precision through trained algorithms while dramatically improving productivity by automating the detection process across entire geographic regions.
Solution Approach 2:
The system enables self-service detection where the Machine Learned Model automatically processes images, identifies features, converts pixel measurements to real-world dimensions, and generates quantification results without continuous human intervention. The model learns from training data and independently performs detection and measurement tasks.
2Loss of information
If comprehensive geographic analysis is performed across large regions, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The patent segments the geographic region into multiple image tiles captured from street-level viewpoints. Each tile is processed independently by the Machine Learned Model, allowing comprehensive coverage of large areas while managing system complexity through modular processing. The segmented approach enables parallel processing and reduces the computational burden on any single processing unit.
Solution Approach 2:
The Machine Learned Model serves multiple functions: it detects various exterior features (doors, windows, façades), quantifies their dimensions, converts pixel measurements to real-world units, and generates standardized output data. This multi-functionality reduces system complexity by consolidating multiple detection and measurement tasks into a single unified system.
Data Source
AI summary
A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.