Automated Property Valuation via Aerial Imagery and Machine Learning
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Solution Overview
Problem
The existing process of inventorying and valuing real property is time-consuming and resource-intensive, requiring manual site visits and data collection for each property, which is inefficient for large-scale municipal tax reassessments.
Innovation Solution
A method and system utilizing aerial imagery from drones or property record cards, combined with machine learning and computer vision, to automate the process of creating accurate property sketches and valuations by integrating aerial and satellite imagery with street-level data, allowing for automated property inventory and valuation without on-site inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual site visits and data collection are performed for each property, then measurement precision and reliability are improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system creates digital copies of properties through aerial imagery and structure sketches, replacing the need for physical site visits. The computer-generated 3D models and aerial photographs serve as accurate replicas that capture property characteristics without requiring manual inspection, thus maintaining measurement precision while dramatically improving productivity
Solution Approach 2:
The patent replaces the mechanical process of manual data collection with an automated computer vision system. Machine learning algorithms automatically analyze aerial imagery to extract property features, calculate square footage, and generate valuations, substituting human inspectors with an automated digital system that operates faster and at scale
2Productivity
If automated processing is implemented, then productivity and time efficiency are improved, but measurement precision may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where automated valuation results are continuously refined based on comparisons with known property data and adjustment factors. The computer vision system learns from validated cases and improves its precision over time, ensuring that automated processing maintains or enhances measurement accuracy while preserving high productivity
Solution Approach 2:
The patent performs preliminary data collection and validation by gathering aerial imagery and existing property records before the main valuation process. This preparatory work ensures that the automated system has access to accurate baseline data, improving measurement precision while maintaining the efficiency benefits of automation
3Measurement precision
If comprehensive property inspection is performed, then measurement precision is improved, but loss of time and resource intensity worsen
Solution Approach 1:
The system segments the property inspection process into distinct automated components: aerial imagery capture, computer vision analysis, structure sketch generation, and valuation calculation. Each segment is processed independently and automatically, maintaining comprehensive inspection quality while eliminating the time loss associated with sequential manual inspection steps
Data Source
AI summary
A method and system of inventorying and developing value for real property. An image or an aerial photograph of a property to be inventoried and valued is obtained, an outline or outlines of the structure and its location on the property are identified, outlines of the structural sections of the property are mapped, a partial sketch of the outline of the structure is created, wherein the outline includes square footage estimates for each section, and is oriented with the front of the structure to the bottom of the page, and the information is integrated across multiple images and the structural type of each section of the structure is identified. The method and system rely on machine learning in order to increase the accuracy of the method and system.