Geographic Location Encoding Model Using Function and Surface Feature Embedding
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Solution Overview
Problem
Conventional geographic location encoding methods are unreasonable as they primarily rely on physical proximity, failing to effectively distinguish regions with similar geographic functions and surface-feature distributions.
Innovation Solution
A method and apparatus for encoding geographic location regions using an encoding model that trains on triplets of anchor, positive, and negative samples, embedding geographic function and surface-feature distribution information to produce encoding results that reflect similarity in functions and distributions, rather than physical proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional geographic location encoding methods based on physical proximity are used, then encoding simplicity is maintained, but the ability to distinguish regions with similar geographic functions and surface-feature distributions deteriorates
Solution Approach 1:
The patent changes the encoding parameters from simple physical proximity metrics to multi-dimensional features including geographic function information and surface-feature distribution information. This allows regions with similar functions and distributions to be distinguished even when physically distant, resolving the contradiction between encoding simplicity and region distinction accuracy.
Solution Approach 2:
The patent introduces additional encoding dimensions beyond physical proximity by incorporating geographic function information and surface-feature distribution information. This multi-dimensional approach enables better region distinction while maintaining reasonable encoding complexity through structured feature processing.
2Reliability
If encoding based on geographic function information and surface-feature distribution information is implemented, then region grouping合理性 is improved, but computational complexity increases
Solution Approach 1:
The patent segments the encoding process into distinct modules: acquiring geographic function information, acquiring surface-feature distribution information, embedding processing, and fusion. This segmentation makes the complex computational task more manageable and efficient while maintaining high encoding reliability through comprehensive feature processing.
Solution Approach 2:
The patent performs preliminary embedding processing on geographic function information and surface-feature distribution information separately before fusion. This preliminary action organizes the data structure in advance, reducing computational complexity during the actual encoding process while ensuring reliable region grouping.
Data Source
AI summary
A method and apparatus for encoding a geographic location region as well as a method and apparatus for establishing an encoding model, which relate to big data and deep learning technologies in the field of artificial intelligence technologies are disclosed. An implementation includes: determining a to-be-encoded geographic location region; acquiring at least one kind of geographic function information and at least one kind of surface-feature distribution information of the geographic location region; and inputting the acquired geographic function information and the acquired surface-feature distribution information into an encoding model, the encoding model performing embedding on the geographic function information and the surface-feature distribution information, and fusing vector representations obtained by the embedding to obtain an encoding result of the geographic location region.