Property Image Scoring for Automated Blight Violation Detection
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Solution Overview
Problem
Current methods for detecting housing blight and property code violations rely on manual labor, which is costly, uneven, and poses risks to inspectors, and may result in incomplete detection due to reliance on citizen complaints and physical property inspections.
Innovation Solution
An automated system using a machine learning model trained on photographs of properties with known blight or code violations, which generates scores for detected properties, reducing the need for manual inspections and enabling automated data collection from municipal vehicles like garbage trucks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by inspectors is used, then detection accuracy is improved, but inspection costs increase
Solution Approach 1:
The patent replaces the mechanical inspection system (human inspectors physically visiting properties) with an automated image analysis system using machine learning models. Municipal vehicles equipped with cameras capture property images, which are then analyzed by trained models to detect blight and code violations, eliminating the need for manual inspector deployment while maintaining detection capability.
Solution Approach 2:
The system creates visual copies (photographs) of properties as substitutes for physical inspection. These images serve as digital representations that can be analyzed remotely by machine learning models, replacing the need for inspectors to physically examine each property while preserving the essential inspection function.
2Reliability
If physical property visits by inspectors are performed, then verification of blight conditions is improved, but inspector safety deteriorates
Solution Approach 1:
The patent replaces physical inspector presence with remote automated analysis. Cameras mounted on municipal vehicles capture images of properties from a safe distance, and machine learning models perform the verification function without requiring human inspectors to enter potentially dangerous environments, thus eliminating safety risks while maintaining verification capability.
Solution Approach 2:
The system introduces an intermediary layer (machine learning model analyzing images) between the inspection objective and human inspectors. This intermediary performs the hazardous verification work remotely, shielding inspectors from direct exposure to dangerous conditions while still achieving reliable detection of blight and code violations.
3Productivity
If citizen complaints are used for blight detection, then detection coverage is improved in engaged neighborhoods, but detection uniformity deteriorates
Solution Approach 1:
The patent implements a universal detection system using municipal vehicles that already traverse all neighborhoods performing their regular functions. These vehicles are equipped with cameras to capture property images uniformly across the entire city, replacing the non-uniform citizen complaint system with a consistent automated inspection approach that covers all areas equally regardless of citizen engagement levels.
Solution Approach 2:
The system enables the municipal vehicle fleet to perform dual functions: their primary service function and automated blight detection. The vehicles autonomously capture and transmit property images along their predetermined routes without requiring citizen initiation, creating a self-driven detection system that uniformly covers all neighborhoods regardless of community engagement.
Data Source
AI summary
A system for blight and code violation detection is provided. The system includes a machine learning model that is trained using photographs of properties that are associated with blight and code violations and photographs that are not associated with blight and code violations. A fleet of vehicles, such as trash trucks, are equipped with cameras to take photographs of properties along their routes. The trained model may generate a score for each photograph that indicates whether or not a property is blighted or has code violations. Those properties with a score that exceeds a threshold may be provided to a reviewer who may verify the finding. If the finding is verified, the reviewer may issue a letter or citation to the owner of the property, and positive feedback may be provided to the model. If the finding is not verified, negative feedback may be provided to the model.