Multi-Scale Image Embedding Fusion for Parcel Risk Assessment
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Solution Overview
Problem
Existing risk assessment systems for natural disasters, such as wildfires, struggle to efficiently account for features at multiple scales using processor- and memory-intensive techniques like feature pyramids, which provide marginal improvements with technical costs.
Innovation Solution
A machine learning system utilizing multiple ML models, each tailored to different image scales and resolutions, processes parcel-, neighborhood-, and landscape-level embeddings to provide a risk assessment through a fusion network, enabling efficient and accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature pyramids are used to account for features at multiple scales, then measurement precision is improved, but device complexity and computational resource consumption increase significantly
Solution Approach 1:
The patent divides the risk assessment system into multiple independent ML models, each specialized for a specific image scale (parcel-level, neighborhood-level, landscape-level). This segmentation allows each model to focus on features at its designated scale without the computational overhead of processing all scales in a single complex model, thereby maintaining measurement precision while reducing device complexity.
Solution Approach 2:
The fusion network serves as a universal component that integrates feature embeddings from multiple specialized ML models operating at different scales. This multi-functional architecture allows the system to handle features at various scales through a unified framework, improving risk assessment accuracy without proportionally increasing system complexity.
2Measurement precision
If feature pyramids are used to account for features at multiple scales, then measurement precision is improved, but processor and memory resources increase
Solution Approach 1:
By segmenting the processing task across multiple specialized ML models, each model processes only the feature embeddings relevant to its scale. This reduces the computational resource consumption compared to a single model attempting to process all scales, while still achieving improved measurement precision through the comprehensive multi-scale analysis.
Solution Approach 2:
The system extracts only the necessary feature embeddings from each image scale using specialized ML models, rather than processing entire images through a single complex model. This extraction approach reduces processor and memory resource requirements while maintaining the precision benefits of multi-scale analysis.
3Productivity
If multiple ML models process different image scales, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the processing workflow into distinct stages, with each ML model handling a specific image scale. This segmentation improves productivity by allowing parallel processing of different scales and reducing the computational burden on each individual model, while the modular architecture manages device complexity through clear separation of concerns.
Solution Approach 2:
Feature embeddings serve as intermediaries between the specialized ML models and the fusion network. This intermediary mechanism allows multiple models to operate independently at different scales while maintaining a relatively simple integration framework, thereby improving productivity without proportionally increasing device complexity.
Data Source
AI summary
Methods, systems, and apparatus for receiving a request for a risk assessment for a parcel, receiving a set of images for the parcel, the set of images including two or more images, each image having an image scale and an image resolution that is different from other images in the set of images, providing a first-level feature embedding and a second-level feature embedding, the first-level feature embedding being provided by processing a first-level image through a first-level machine learning (ML) model, and the second-level feature embedding being provided by processing a second-level image through a second-level ML model, determining a risk assessment at least partially by processing each of the first-level feature embedding and a second-level feature embedding through a fusion network, and providing a representation of the risk assessment for display.