Panoramic Street View Modeling for Road Traffic Carbon Emissions
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Solution Overview
Problem
Conventional methods for quantifying road traffic carbon emissions are inaccurate and lack spatial resolution, failing to reflect actual emissions and changes within urban environments.
Innovation Solution
A method and apparatus utilizing panoramic images to construct a carbon emission prediction model through ensemble learning, incorporating feature analysis and geographic mapping, enabling refined predictions and visualization of carbon emission distribution patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical methods based on vehicle mileage and emission factors are used, then the calculation process is simple, but the accuracy of carbon emission results is low and cannot reflect actual emissions
Solution Approach 1:
The patent introduces panoramic street view images as an intermediary medium between vehicle traffic data and carbon emission calculations. These images capture visual features of the road environment, which are then processed through computer vision models to extract meaningful features that correlate with actual carbon emissions, thereby improving measurement accuracy without requiring direct emission sensors
Solution Approach 2:
The patent replaces conventional statistical calculation methods with a machine learning-based prediction model. Instead of using simple emission factors multiplied by mileage, the system employs ensemble learning models (Random Forest, Gradient Boosting, XGBoost) that process both traffic data and image features to predict carbon emissions, substituting mechanical calculation with intelligent prediction
2Measurement precision
If conventional observation technology is used, then the measurement method is straightforward, but the spatial resolution of carbon emission calculation is insufficient and cannot meet requirements for studying distribution patterns
Solution Approach 1:
The patent segments the urban area into multiple observation regions or grid cells, with each region having its own panoramic images and carbon emission predictions. This segmentation enables high spatial resolution analysis of carbon emission distribution patterns across different areas, allowing researchers to study local variations and hotspots rather than treating the entire urban area as a single unit
Solution Approach 2:
The patent adds a spatial dimension to carbon emission calculations by incorporating panoramic image data that captures the physical environment context. This transforms the calculation from a one-dimensional traffic-based estimate to a multi-dimensional assessment that includes visual features of the surrounding area, enabling high-resolution spatial mapping of emissions
3Loss of information
If conventional methods are used, then the observation process is simple, but changes of traffic carbon emissions inside the road cannot be observed
Solution Approach 1:
The patent performs preliminary action by capturing panoramic street view images that document the physical environment and traffic conditions at specific locations and times. These images are processed to extract features that serve as indicators of carbon emission levels, creating a historical record that can be used to detect changes in emissions over time without requiring continuous real-time monitoring equipment
Data Source
AI summary
A method and an apparatus for predicting a road traffic carbon emission based on panoramic images, a device and a medium are provided, relating to the technical field of data prediction. In the method, a historical street view image of an observation area is acquired from the Internet, feature analysis is performed on the historical street view image to obtain a historical feature vector, and a road traffic carbon emission predicted value is obtained based on the historical feature vector and a carbon emission prediction model. With the method or the apparatus, auxiliary explanations for carbon emission sources in cities can be provided based on features of street views.