Satellite Transport Network Mapping from LULC and Vehicle Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing geographical information systems lack the ability to provide up-to-date and detailed information on transport network characteristics, such as road types and traffic conditions, using satellite imagery.
Innovation Solution
A computer-implemented method that combines Land Use/Land Cover (LULC) attribution with transport network information by analyzing co-registered satellite images to identify and determine characteristics of transport networks, including vehicle counts, lane numbers, and traffic directions, using sensors with different wavelength characteristics to detect object displacements and velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional LULC classification methods are used, then classification of land cover types is achieved, but detailed transport network characteristics and traffic patterns cannot be obtained
Solution Approach 1:
The patent merges LULC classification with transport network analysis by integrating object identification and transport route identification within the same image processing framework. This combination allows simultaneous extraction of both land cover information and transport network characteristics from satellite images, eliminating information loss without requiring separate complex systems.
Solution Approach 2:
The system performs multiple functions using the same satellite image data: LULC classification, object identification, transport route mapping, and traffic pattern analysis. This multi-functionality reduces information loss by extracting diverse transport network characteristics from a single data source without proportionally increasing system complexity.
2Loss of time
If satellite images are analyzed frequently to obtain up-to-date transport network information, then information currency is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary identification of transport routes and characteristics from satellite images, storing this information for future use. When new satellite images become available, only updates and changes need to be processed, rather than complete re-analysis, thus maintaining information currency while reducing repeated processing overhead.
Solution Approach 2:
The patent implements periodic analysis of satellite images at optimized intervals, balancing information currency with processing efficiency. By analyzing images periodically rather than continuously, the system maintains up-to-date transport network information while avoiding excessive computational resource consumption.
3Measurement precision
If multiple satellite images are processed to determine transport network characteristics, then measurement precision is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image processing into distinct modules: LULC classification, object identification, transport route identification, and characteristic determination. Each module processes specific aspects independently, improving measurement precision through specialized analysis while reducing overall processing complexity through modular organization and reusability.
Data Source
AI summary
The present disclosure relates to a computer implemented method (100) for attribution of Land Use/Land Cover, LULC, said method comprising the steps of: providing (S1) a plurality of images covering a transport network part, identifying (S2) objects in at least a subset of said plurality of images, identifying (S4) transport routes at least in said at least one satellite image of the plurality of images, and determining (S5) characteristics of said transport network part based on the identified objects at said identified (S4) transport routes.