Remote Architectural Feature Elevation Using LiDAR and Machine Learning
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Solution Overview
Problem
Existing methods for determining the elevation and orientation of architectural features of structures are inefficient, time-consuming, and prone to errors, especially in densely populated areas, making it difficult to assess flood risks accurately and timely.
Innovation Solution
A machine learning model utilizing LiDAR data and other location context data to predict the elevation and orientation of architectural features without direct measurement, integrating data from various sources to generate a predicted feature position data set for user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional door-to-door surveying methods are used to determine architectural feature elevation and orientation, then measurement precision can be maintained, but productivity is significantly reduced and loss of time increases
Solution Approach 1:
The patent replaces traditional mechanical surveying methods (physical presence of surveyors, manual instruments) with an automated optical/electronic system using LiDAR technology. The LiDAR system remotely captures elevation and orientation data of architectural features without requiring physical access to each property, thereby maintaining measurement precision while dramatically improving productivity and reducing time loss.
Solution Approach 2:
The patent introduces LiDAR technology as an intermediary between the surveyor and the target architectural features. The LiDAR system acts as a remote mediator that captures precise spatial data through laser ranging, eliminating the need for direct physical measurement while preserving accuracy. This intermediary approach allows surveys to be conducted from a distance, improving efficiency without sacrificing measurement quality.
2Measurement precision
If traditional surveying methods are used, then data accuracy can be ensured, but device complexity and operational difficulty increase
Solution Approach 1:
The patent employs a universal LiDAR-based platform that can determine both elevation and orientation of various architectural features (roofs, walls, foundations) using the same core technology. This multi-functional system replaces multiple specialized surveying instruments and methods, reducing overall system complexity while maintaining data accuracy across different measurement types.
Solution Approach 2:
The patent creates accurate digital copies (3D point clouds and models) of physical architectural features through LiDAR scanning. These digital replicas preserve precise geometric information without requiring complex physical measurement equipment. The copied spatial data can be analyzed computationally, simplifying the measurement process while maintaining accuracy.
3Reliability
If manual surveys are conducted to assess flood risks, then reliability of risk assessment can be maintained, but loss of time and productivity decrease
Solution Approach 1:
The patent performs preliminary LiDAR data collection and processing to create accurate elevation models of architectural features before flood risk assessment is needed. This advance preparation of spatial data allows rapid flood risk evaluation when required, maintaining reliability while reducing the time loss associated with conducting surveys at the moment of assessment.
Solution Approach 2:
The patent establishes a continuous LiDAR data collection and processing system that maintains updated elevation and orientation information for architectural features. This continuous availability of accurate spatial data ensures reliable flood risk assessment can be performed immediately when needed, eliminating the time delay associated with conducting new surveys for each assessment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and accurate prediction of architectural feature elevations, facilitating effective flood risk assessment and resource allocation by providing real-time data without the need for manual surveys.
Implementation Method 1
transmitting a plurality of ranging signals to respective targets and receiving a plurality of corresponding return signals reflected from the targets
Implementation Method 2
receiving a plurality of corresponding return signals reflected from the targets
Data Source
AI summary
An apparatus, method, and computer program product are provided for the improved and automatic prediction of an elevation of an architectural feature of a structure at a particular geographic location. Some example implementations employ predictive, machine-learning modeling to facilitate the use of LiDAR-derived ground-elevation data, additional location context data, and elevation data from comparator locations to extrapolate and otherwise predict the elevation or other position of a given architectural feature of structure.