Remote Property Inspection Using Multi-View Building Recognition
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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Existing insurance and reinsurance valuation methods for commercial properties suffer from unreliable and outdated data, leading to errors in property classification and significant premium leakage, with misclassification rates as high as 32.5% for construction type and 80% for building area, resulting in undervaluing or overvaluing of insurance premiums.
Innovation Solution
A system utilizing multiple computer-based models to analyze different perspective images of properties, including overhead, ground-level, and oblique views, to accurately identify and characterize building structures and attributes, enhancing data accuracy through image recognition and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual review of photographs or satellite data is used to evaluate property attributes, then data collection can be performed remotely without site visits, but the accuracy and reliability of property classification deteriorates with misclassification rates as high as 32.5% for construction type and 80% for building area
Solution Approach 1:
The patent combines multiple data sources including aerial photographs, satellite imagery, street-level views, and public records into a unified analysis system. By merging these diverse data sources and processing them together through machine learning models, the system achieves both remote operation capability and high classification accuracy, resolving the contradiction between ease of operation and measurement precision.
Solution Approach 2:
The patent replaces manual inspection methods with automated machine learning models that process images and data. This substitution eliminates human error and subjectivity in property classification while maintaining remote operation capability, thereby improving measurement precision without sacrificing ease of operation.
2Reliability
If in place inspections by representatives are conducted to assess property attributes, then data reliability improves through direct visual assessment, but time consumption and operational complexity increase due to required site visits
Solution Approach 1:
The patent performs preliminary analysis by automatically processing aerial photographs, satellite imagery, and other data sources before final property classification. This preliminary action extracts key features and pre-classifies properties, reducing the need for time-consuming site visits while maintaining data reliability through multiple verification steps.
Solution Approach 2:
The system enables properties to effectively self-assess through automated analysis of publicly available imagery and data. Machine learning models independently evaluate property attributes without requiring human inspectors, achieving both high reliability and time efficiency by eliminating the need for manual site visits.
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 with the identified building structure.