Machine Learning Map Generation With Neighboring Tile Context
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Solution Overview
Problem
Developing machine learning models to understand and extract spatial/semantic relationships from images for map generation presents significant technical challenges due to noisy and biased data, skewed distributions, and spartan ground truth data, leading to inconsistent map outputs.
Innovation Solution
An encoder-decoder stack architecture, specifically a deep fully convolutional neural network, is used to process raw data into semantically useful information, focusing on decoding side to generate consistent map representations by leveraging data regularity and invariance, with independent encoders and decoders for different data modalities, and employing sliding windows to integrate neighboring tile features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are trained to extract spatial/semantic relationships from images for map generation, then map generation capability is improved, but data noise and bias cause inconsistent map outputs
Solution Approach 1:
The patent introduces an encoder-decoder architecture as an intermediary system between raw image data and map outputs. The encoder extracts features while the decoder reconstructs spatial relationships, mediating the transformation process to reduce the direct impact of noise and bias on final map consistency
Solution Approach 2:
The patent segments the map generation process into independent encoder and decoder components that can be trained separately. This segmentation allows each component to specialize in specific tasks (feature extraction vs. spatial reconstruction), improving overall reliability while maintaining versatility
2Reliability
If encoder-decoder architecture with sliding windows is used to process image tiles, then map representation consistency is improved, but computational complexity increases
Solution Approach 1:
The patent divides the input image into multiple tiles and processes them independently through sliding windows. This segmentation reduces the computational burden on any single processing unit while maintaining overall map consistency through overlapping window operations that capture contextual relationships
Solution Approach 2:
The sliding window approach applies partial processing to overlapping regions of tiles. By processing slightly more data than strictly necessary (overlapping windows), the system ensures consistency at tile boundaries while distributing computational load across multiple windows
Data Source
AI summary
An approach is provided for machine learning-based map generation. The approach involves, for example, receiving an encoder output for an image depicting a geographic area, wherein the encoder output comprises an encoding for each tile of a plurality of tiles of the image, and wherein the encoding represents data associated with a location of each tile. The approach also involves using a machine learning decoder to determine a window over the encoder output comprising a tile of the plurality of tiles and one or more neighboring tiles and to process the encoding associated with the tile and the one or more neighboring tiles in the window to generate a map representation for the location of the tile and providing the map representation as an output.