Automated Parcel Change Detection Using Imagery Analysis
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Solution Overview
Problem
Current methods for detecting property changes in imagery are labor-intensive, costly, and inefficient, often missing changes not visible from the street or overhead, and require manual comparison of old and new imagery, which is time-consuming and prone to errors.
Innovation Solution
A computer-implemented method that automatically classifies property changes by analyzing differences between old and new imagery, using image processing components to identify changes and provide a classification, with manual review and feedback mechanisms to improve accuracy, and generates reports with tax impact analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of imagery is used to detect property changes, then accuracy can be maintained through human judgment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical human inspection process with an automated computer vision system that uses machine learning models to detect property changes in imagery. The system automatically compares current imagery with historical data, identifies changes, and generates reports without requiring manual review of each property, thereby maintaining detection accuracy while dramatically reducing inspection time.
Solution Approach 2:
The patent introduces an automated change detection system as an intermediary between imagery collection and assessment decision-making. This intermediary system pre-processes imagery data, identifies potential changes, and prioritizes properties for further review, allowing human assessors to focus only on complex or uncertain cases rather than manually inspecting every property.
2Measurement precision
If field assessors physically visit properties to detect changes, then accurate verification is possible, but safety risks and costs increase
Solution Approach 1:
The patent replaces physical field visits with automated imagery analysis systems that process aerial, satellite, and street-level photographs. The system uses computer vision algorithms to detect structural changes, additions, and modifications without requiring assessors to physically access properties, thereby eliminating safety risks associated with visiting potentially hazardous locations while maintaining verification accuracy through multiple imagery sources.
Solution Approach 2:
The patent transitions from two-dimensional street-level views to three-dimensional oblique imagery and aerial perspectives, enabling detection of property changes that are invisible from ground level. This dimensional shift allows the system to identify changes on the sides and roofs of structures without physical contact, maintaining accuracy while avoiding safety hazards.
3Measurement precision
If comprehensive imagery collection from multiple angles is performed, then detection accuracy improves, but data processing complexity and costs increase
Solution Approach 1:
The patent segments the complex task of multi-angle imagery analysis into distinct processing stages: initial automated screening using simple algorithms, followed by focused analysis of identified changes using more sophisticated computer vision models. The system divides imagery data into relevant and irrelevant portions, processing only the segments that contain potential changes, thereby reducing overall system complexity while maintaining high detection accuracy.
Solution Approach 2:
The patent performs preliminary automated processing of imagery data before detailed analysis, using quick computational methods to identify and flag potential changes. This preliminary action filters out unchanged properties and obvious false positives, reducing the volume of data that requires complex processing and human review, thereby managing system complexity while preserving detection accuracy.
4Productivity
If automated systems are used to reduce labor costs, then productivity increases, but detection accuracy may decrease without manual review
Solution Approach 1:
The patent implements an automated change detection system as an intermediary that handles the bulk of property analysis at high speed, then presents prioritized results to human reviewers for verification. This intermediary layer maintains productivity by automating routine detection while preserving accuracy through selective human review of uncertain or complex cases, achieving both high throughput and high precision.
Solution Approach 2:
The patent incorporates feedback mechanisms where automated detection results are reviewed and validated by human assessors, and this feedback is used to continuously train and improve the automated system's algorithms. The system learns from correction feedback, gradually improving classification accuracy while maintaining high productivity through automated processing of confirmed changes.
Data Source
AI summary
A computer-implemented method for identifying property changes by analyzing differences between old and new imagery. In one embodiment, an administrator begins by creating a project with links to old and new imagery and uploading a file with parcel boundaries. The application may then divide the project into smaller batches and then automatically classify each batch. Analysts may then review these batches and reject those that are insufficiently accurate. For rejected batches, analysts may manually classify the parcels. Analysts may then review these batches again. In one embodiment, once all batches are accepted, the project may be marked as complete and suitable reports may be generated and sent. These reports may include a customer report which may include overall project statistics, a breakdown of changes by type, a heatmap showing which areas are changing fastest, and/or an estimated tax impact of the identified changes.