Automated Property Inspection With Multi-View Building Analysis
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Solution Overview
Problem
Existing insurance and reinsurance valuation methods for commercial properties suffer from unreliable and outdated data, leading to errors in property classification and premium leakage, with significant undervaluing or overvaluing of insurance premiums.
Innovation Solution
A system utilizing multiple computer-based models to analyze different perspective images of properties, including overhead and ground-level views, to accurately identify and characterize building structures and attributes, enhancing the accuracy of insurance assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual review of photographs or satellite data is used to evaluate property attributes, then data collection cost and time are reduced, but measurement precision and reliability of property classification deteriorate
Solution Approach 1:
The patent replaces manual inspection (mechanical human effort) with automated image recognition systems using machine learning models. The system automatically analyzes multiple perspective images to extract property attributes, eliminating the need for manual photo review while maintaining or improving classification accuracy through systematic algorithmic processing.
Solution Approach 2:
The patent creates multiple copies of property views from different perspectives (aerial, street-level, ground-level images) and analyzes them collectively. By evaluating multiple image copies rather than a single view, the system achieves more reliable property classification while still avoiding the time cost of physical inspections.
2Measurement precision
If in place inspections by representatives are conducted to collect property data, then measurement precision and reliability improve, but productivity and loss of time deteriorate
Solution Approach 1:
The patent substitutes physical inspector visits with automated image analysis systems. Multiple perspective images are processed by machine learning models to extract construction type, occupancy, protection, and exposure attributes, achieving comparable or superior accuracy without the time loss of traveling to and inspecting each property physically.
Solution Approach 2:
The patent performs preliminary data collection by obtaining multiple perspective images before the actual analysis process. These pre-collected images from aerial photography, street view services, and other sources are ready for automated processing, eliminating the need for inspectors to physically visit properties and collect data during inspection visits.
3Measurement precision
If multiple perspective images are analyzed using computer-based models to accurately classify properties, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent segments the complex task of property classification into multiple specialized machine learning models, each trained to analyze specific image perspectives (aerial views, street-level views, ground-level views). This segmentation allows each model to focus on particular visual features from its designated perspective, improving overall classification accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal property classification system that handles multiple property types and attributes through a single integrated platform. The machine learning models are designed to evaluate construction type, occupancy, protection, and exposure attributes across diverse property categories, reducing the need for separate specialized systems for different property classes.
Data Source
AI summary
A property inspection service hosted on a web-based server system. The server system receives a list of physical addresses each corresponding to different parcels. For each address, the server system obtains multiple images including overhead images and perspective view images. A first trained model analyzes selected overhead images individually to identify one or more building structures. A second trained model analyzes selected perspective view images individually to identify a primary building structure. A third trained model analyzes the selected overhead images and the perspective view images together in an integrated approach to identify attributes associated with identified building structure. A digital report is generated as a graphical user interface configured to display the selected images and the attributes associated the identified building structure.