Distributed Vector-Raster Fusion via Columnar Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current geospatial software solutions lack scalability and distributed analytics capabilities to effectively fuse and process petabytes of vector data with raster data, limiting their ability to support growing amounts of vector data generated from sources like LiDAR, autonomous driving, and satellite imagery.
Innovation Solution
A method and system that receive vector data, statistically analyze it, rasterize the statistics, and store both vector data and statistics in a key-value store alongside previously stored raster data for a geographical location, enabling integrated analytics and fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vector data is stored and processed separately from raster data, then data integrity is maintained, but processing efficiency and analytics capability deteriorate due to inability to fuse petabytes of data
Solution Approach 1:
The patent merges vector data and raster data into a unified columnar storage format, allowing both data types to coexist in the same storage structure. This enables efficient fusion of petabytes of data by eliminating the need for separate storage systems while maintaining the ability to process and analyze both vector and raster data together through distributed analytics engines.
Solution Approach 2:
The columnar storage system is designed to handle multiple data types (vector and raster) and support multiple analytics operations (spatial joins, fusion, machine learning) within a single platform. This universal approach allows the system to process diverse geospatial data formats and perform various analytics tasks without requiring separate specialized systems.
2Adaptability or versatility
If distributed analytics are implemented for vector data, then scalability is improved, but system complexity increases
Solution Approach 1:
The patent segments the geospatial data storage and processing system into columnar storage units that can be independently distributed across multiple nodes. By organizing data into columnar formats and using distributed file systems, the system achieves scalability by adding storage capacity and processing power through simple node addition rather than complex system redesign.
3Productivity
If vector data is rasterized for fusion with raster data, then data integration is improved, but information loss may occur
Solution Approach 1:
The patent changes the storage parameter from traditional row-based or grid-based formats to columnar format, which allows vector data to maintain its precise geometric representation while enabling efficient fusion operations with raster data. The columnar structure preserves vector precision by storing coordinate information in a way that maintains spatial relationships without requiring conversion to raster format.
Data Source
AI summary
In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.