Satellite Road Obstruction Detection for Disaster Route Assessment
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Solution Overview
Problem
Existing technologies fail to promptly determine traversable roads during disasters, as they require a vehicle to travel on a road to assess its traversability, leading to potential delays and risks.
Innovation Solution
An information processing device that acquires satellite images before and after a disaster, detects road obstructions using image analysis, and prioritizes roads based on luminance and travel history to suggest traversable routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a vehicle travels on a road to determine traversability, then the road condition can be confirmed through direct experience, but the time required to propose a traversable road increases significantly during disasters
Solution Approach 1:
The patent uses satellite images as a copy or representation of the actual road condition, allowing remote observation of road status without physical vehicle traversal. The satellite imagery captures visual information about road obstructions, flooding, and damage, enabling traversability assessment from a distance.
Solution Approach 2:
The patent introduces satellite images as an intermediary medium between the vehicle and the road condition. Instead of direct vehicle-road interaction for assessment, the system uses satellite-captured imagery as a mediator to convey road status information, enabling indirect but timely observation.
2Loss of time
If satellite images are used to detect road obstructions, then the time to propose traversable roads is reduced, but the precision of obstruction detection may be insufficient compared to ground-level observation
Solution Approach 1:
The patent employs deep learning models that have been trained to perform multiple functions: detecting various types of obstructions (debris, flooding, vehicle blockages), assessing road damage, and determining traversability. This multi-functional approach allows the system to handle diverse disaster scenarios with a unified detection framework.
Solution Approach 2:
The patent replaces traditional mechanical or manual road assessment methods with automated image processing and deep learning algorithms. The system substitutes physical vehicle traversal and human visual inspection with computational analysis of satellite imagery, achieving both speed and accuracy through algorithmic detection.
3Measurement precision
If multiple satellite images (before and after disaster, daytime and nighttime) are acquired and compared, then the accuracy of obstruction detection improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the image analysis process into distinct functional modules: pre-disaster and post-disaster image comparison, daytime and nighttime image processing, different detection models for different road sections. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent performs preliminary actions by acquiring and processing satellite images before disasters occur, establishing baseline road conditions and training detection models in advance. Pre-disaster images serve as reference data for comparison, and the system prepares detection algorithms beforehand to enable rapid post-disaster assessment.
Data Source
AI summary
Provided is an information processing device that includes at least one processor, wherein the at least one processor is configured to: acquire a satellite image of the ground, which is captured by an artificial satellite, for a target area based on disaster information; based on the satellite image, detect an obstruction on a road which relates to passage of a vehicle; and output a road for which no obstruction is detected as a traversable road.