AI UAV Accessibility Mapping System
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Solution Overview
Problem
Current methods for auditing the accessibility of building and public spaces are inefficient and costly, requiring manual human intervention to identify accessible and inaccessible features, which hinders the ability to provide universal access for all individuals, including those with disabilities.
Innovation Solution
An AI-assisted unmanned aerial vehicle (UAV) system utilizing Geographic Information Systems (GIS) and machine learning algorithms to automatically map and identify accessible and inaccessible features in a site, such as pathways, amenities, and seating areas, by capturing geospatial imaging data and generating site maps that denote these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human intervention is used to identify accessible and inaccessible features, then measurement precision can be maintained, but productivity is reduced and loss of time increases
Solution Approach 1:
The patent replaces manual human inspection with an automated system comprising a mobile platform, imaging sensors, and AI processing. The system captures images of pathways and amenities, then uses machine learning models to automatically identify accessible and inaccessible features, eliminating the need for manual assessment while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The patent introduces an intermediary AI processing layer between the physical environment and the accessibility assessment. The mobile platform captures images, which are then processed by trained machine learning models that act as intermediaries to identify accessibility features, bridging the gap between raw visual data and meaningful accessibility evaluation.
2Measurement precision
If manual auditing methods are used, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The mobile platform continuously captures images of the building site as it moves through the area, enabling uninterrupted data collection. The AI processing system continuously analyzes images and identifies accessibility features in real-time, maintaining continuous useful action from data capture to assessment without manual intervention pauses.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on large datasets of accessible and inaccessible features before deployment. This preliminary training enables the system to rapidly and accurately identify features during the actual audit without time-consuming manual analysis during the assessment process.
3Productivity
If automated AI systems are deployed, then productivity increases and loss of time decreases, but device complexity increases
Solution Approach 1:
The mobile platform is designed as a universal system capable of performing multiple functions: capturing images of various accessibility features, processing diverse image data, identifying different types of pathways and amenities, and generating comprehensive accessibility assessments. This multi-functionality consolidates what would otherwise require multiple specialized tools into a single integrated system.
Solution Approach 2:
The system uses trained machine learning models that are essentially copied versions of expert human assessment capabilities. The AI models are trained on extensive datasets to replicate the decision-making processes of accessibility experts, allowing the system to perform complex assessments without requiring actual experts to be present during the audit.
Data Source
AI summary
The present disclosure presents systems and methods for automatically performing an accessibility audit using Artificial Intelligence (AI) techniques. One such method, among others, comprises acquiring geospatial imaging data of a location site; identifying, using artificial intelligence, accessible features of the location site that are determined to be accessible to a person having a physical disability; identifying, using artificial intelligence, inaccessible features of the location site that are determined to be inaccessible to a person having a physical disability; and/or generating, using Global Information System mapping processes, an aerial map of a location site using the geospatial imaging data, wherein the aerial map comprises a first layer denoting the accessible features of the location site using specific colors, wherein the aerial map comprises a second layer denoting the inaccessible features of the location site using different specific colors.