Virtual Area Visualization With Targeted High-Resolution Imaging
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Solution Overview
Problem
Existing virtual models of large-scale areas are generated at low resolutions, lacking sufficient detail to assess damage to structures or determine route availability, especially in disaster-stricken regions.
Innovation Solution
A system that dispatches remote imaging vehicles to capture high-resolution image data of indicated areas within a larger region, integrating these models into a virtual environment for detailed damage assessment and emergency response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual models of large-scale areas are generated at low resolutions, then the overall region can be visualized, but sufficient detail is lost to assess damage or determine route availability
Solution Approach 1:
The system divides the large-scale region into multiple sub-regions or areas of interest. Each sub-region can be captured and modeled at high resolution by remote imaging vehicles, while the overall region maintains a broader low-resolution context. This segmentation allows high measurement precision for specific areas without sacrificing the ability to visualize the entire large-scale region.
2Loss of information
If remote imaging vehicles are dispatched to capture high-resolution image data of specific areas, then detailed visualization is achieved, but response time and system complexity increase
Solution Approach 1:
The system pre-positions or pre-programs remote imaging vehicles to be ready for rapid deployment to specific areas. When a need for high-resolution imaging arises, the vehicles can be quickly dispatched without extensive preparation time. This preliminary preparation reduces the response time while still enabling detailed visualization when needed.
Solution Approach 2:
The system introduces a coordination server or control system that acts as an intermediary between the need for high-resolution imaging and the remote imaging vehicles. This intermediary efficiently manages vehicle dispatch, routes, and data collection priorities, minimizing the time loss associated with coordinating multiple vehicles and processing requests.
3Measurement precision
If multiple remote imaging vehicles are deployed to capture high-resolution data of multiple areas, then comprehensive detail is obtained, but device complexity and coordination requirements increase
Solution Approach 1:
The remote imaging vehicles are designed with multi-functionality, capable of performing various imaging tasks (aerial, ground, water-based) and adapting to different areas of interest. This universality reduces the number of specialized vehicles needed, thereby lowering overall system complexity while still achieving comprehensive high-resolution coverage of multiple areas.
Solution Approach 2:
The system implements feedback mechanisms where the server receives data about area requirements, damage levels, and route needs, then uses this information to intelligently allocate and coordinate remote imaging vehicles. This feedback-driven coordination optimizes the deployment strategy, reducing unnecessary complexity in vehicle management while ensuring comprehensive high-resolution data collection.
Data Source
AI summary
A computer-implemented method and system for virtual visualization of overall regions are disclosed. Images of an overall region may be obtained by a server and used to generate a virtual model of the overall region. The server may generate a virtual environment that includes the virtual model of the overall region. A user may indicate a specific area of the virtual environment. The server may then dispatch an imaging vehicle to the location of the indicated area to capture additional image data representative of the indicated area. This additional set of image data may be used by the server to generate higher resolution virtual models of the indicated area that are integrated into the virtual environment.