Exterior Image Analysis for Interior Area Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining the livable area of a structure is slow, expensive, and often results in outdated data due to the manual process of measuring interior spaces, which is inefficient and time-consuming.
Innovation Solution
A fully automated machine learning system that analyzes exterior images of a structure using a segmentation model to classify interior areas, estimating total living and non-living areas by projecting exterior features into a coordinate system and generating a segmented classification map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and measurement processes are used to determine livable area, then measurement precision can be maintained, but productivity is reduced and loss of time increases
Solution Approach 1:
The system creates a digital copy (photograph) of the interior space and processes this copy through machine learning models to extract livable area information, eliminating the need for physical inspection while maintaining measurement accuracy
Solution Approach 2:
The patent replaces the mechanical manual measurement process with an automated computer vision system that uses machine learning algorithms to analyze images and determine livable area, significantly improving productivity while maintaining precision
2Measurement precision
If manual inspection processes are used, then detailed interior measurements can be obtained, but loss of time increases due to travel and scheduling requirements
Solution Approach 1:
The system performs preliminary capture of interior space information through photography before any measurement analysis occurs, allowing rapid processing and elimination of travel and scheduling time while preserving measurement capability
3Productivity
If exterior image analysis is used to determine building characteristics, then productivity is improved and loss of time is reduced, but measurement precision deteriorates compared to manual interior measurement
Solution Approach 1:
The system uses high-quality photographic copies of the exterior and applies sophisticated machine learning models to extract accurate livable area information, bridging the gap between remote imaging convenience and measurement precision
Solution Approach 2:
The patent transforms the approach by changing from direct physical measurement to image-based analysis with advanced algorithms, modifying the parameters of the measurement process to achieve both speed and accuracy
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods and systems are disclosed, including a computer system configured to automatically determine home living areas from digital imagery, comprising receiving digital image(s) depicting an exterior surface of a structure with exterior features having feature classification(s) of an interior of the structure; processing the depicted exterior surface into exterior feature segments with an exterior surface feature classifier model, each of the exterior feature segments corresponding to exterior feature(s); project each of the plurality of exterior feature segments into a coordinate system based at least in part on geographic image metadata, the projected exterior feature segments forming a structure model; generate a segmented classification map of the interior of the structure by fitting one or more geometric section into the structure model in a position and orientation based at least in part on the plurality of exterior feature segments.