Distributed Image Analysis via Spatial Indexing and Grid Processing
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Solution Overview
Problem
Current solutions lack the ability to effectively process both raster and vector data in a distributed environment, particularly for georeferenced images, and fail to compute spatial properties of objects based on pixel values, leading to a gap in handling both domains simultaneously.
Innovation Solution
The proposed methods support object-based distributed analysis of digital images by processing image tiles and indexed image objects through a regular grid with a reference coordinate system, enabling partial computation and integration of results across a computer cluster, and include spatial-aware replication strategies for spectral and topological property computation and spatial conflict resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed systems based on MapReduce are used for raster processing, then processing scalability is improved, but spatial awareness and ability to handle georeferenced images is lost
Solution Approach 1:
The patent introduces a spatial indexing layer as an intermediary between the distributed MapReduce system and the georeferenced image data. This spatial index structure enables the distributed system to understand spatial relationships and perform spatial queries while maintaining the scalability benefits of distributed processing.
Solution Approach 2:
The system is designed to handle both raster and vector data types within the same distributed framework, making it universally applicable to multiple data formats and spatial operations. This multi-functionality allows the system to process georeferenced images while maintaining spatial awareness through a unified architecture.
2Ease of operation
If systems like Hadoop-GIS and SpatialHadoop are used for spatial data storage and querying, then spatial query execution is improved, but ability to compute properties based on raster pixel values is lost
Solution Approach 1:
The patent merges the strengths of existing systems by integrating raster processing capabilities with vector spatial indexing in a unified distributed system. This combination allows the system to execute spatial queries efficiently while simultaneously computing properties based on raster pixel values, eliminating the need to choose between the two capabilities.
3Productivity
If AEGIS is ported to Hadoop framework, then some spatial operations are improved, but vector data processing in distributed environment is still not supported
Solution Approach 1:
The patent segments the processing of vector data into distributed tasks that can be executed across the Hadoop cluster. By dividing vector data processing into manageable chunks that can be processed in parallel, the system maintains the distributed processing advantages while enabling vector data operations that were previously unsupported in this environment.
Data Source
AI summary
Methods that support the analysis of digital images through the distributed and integrated processing of raster and vector digital data in a computer cluster environment, the set of methods including a particular strategy for distributing the processing of spatial context-aware operations over distributed datasets, as well as specific methods for the structuring of operations aimed at calculating spectral and topological properties of image objects, and for the resolution of spatial conflicts among objects.